Wireless stroke monitoring

ABSTRACT

A monitoring system includes wireless local area network (WLAN) transceivers operating as a Doppler radar to wirelessly detect the person&#39;s heart parameter; and a processor coupled to the WLAN transceivers to determine a stroke attack.

This application claims priority to Provisional Application 60/939,856,filed May 24, 2007, and U.S. application Ser. No. 11/768,381 (Jun. 26,2007), the contents of which are incorporated by reference.

BACKGROUND

This invention relates generally to methods and systems for monitoring aperson.

Stroke is the third-leading cause of death in the United States. Astroke is defined as a sudden loss of brain function caused by ablockage or rupture of a blood vessel to the brain. Approximately150,000 deaths per year are attributed to stroke. It is also the mostcommon neurologic reason for hospitalization. A stroke occurs when ablood vessel (artery) that supplies blood to the brain bursts or isblocked by a blood clot. Within minutes, the nerve cells in that area ofthe brain are damaged, and they may die within a few hours. As a result,the part of the body controlled by the damaged section of the braincannot function properly. Prior to a stroke, a person may have one ormore transient ischemic attacks (TIAs), which are a warning signal thata stroke may soon occur. TIAs are often called mini strokes becausetheir symptoms are similar to those of a stroke. However, unlike strokesymptoms, TIA symptoms usually disappear within 10 to 20 minutes,although they may last up to 24 hours.

Stroke can be subdivided into two types: ischemic and hemorrhagic.Ischemic stroke accounts for 85% of all cases. In ischemic stroke,interruption of the blood supply to the brain results in tissuehypoperfusion, hypoxia, and eventual cell death secondary to a failureof energy production. Three main mechanisms are involved in thedevelopment of ischemic stroke, and they are associated withatherothrombotic, embolic, and small-vessel diseases. Less common causesinclude coagulopathies, vasculitis, dissection, and venous thrombosis.

Early detection and treatment of stroke is essential to recovery from astroke.

SUMMARY

In one aspect, a heart monitoring system includes an 802 protocoltransmitter; an 802 protocol receiver adapted to communicate with the802 transmitter, the 802 protocol transmitter and receiver forming aDoppler radar to detect heartbeat motion on a chest; and an analyzercoupled to one of the transmitter and receiver to determine heart attackor stroke attack.

In another aspect, a monitoring system for a person includes a wirelesslocal area network (WLAN) transceivers operating as a Doppler radar towirelessly detect the person's heart parameter such as heart rate; and aprocessor coupled to the WLAN transceivers to determine a stroke attack.

In yet another aspect, a health care monitoring system for a personincludes one or more wireless nodes forming a wireless mesh network;WLAN transceivers operating as a Doppler radar to detect heart beat; anda processor coupled to the WLAN transceivers to detect a heart attack ora stroke attack.

In another aspect, a monitoring system for a person includes one or morewireless nodes and a stroke sensor coupled to the person and thewireless nodes to determine a stroke attack.

In another aspect, a health care monitoring system for a person includesone or more wireless nodes forming a wireless mesh network; a wearableappliance having a sound transducer coupled to the wireless transceiver;and a bioelectric impedance (BI) sensor coupled to the wireless meshnetwork to communicate BI data over the wireless mesh network.

In a further aspect, a heart monitoring system for a person includes oneor more wireless nodes forming a wireless mesh network and a wearableappliance having a sound transducer coupled to the wireless transceiver;and a heart disease recognizer coupled to the sound transducer todetermine cardiovascular health and to transmit heart sound over thewireless mesh network to a remote listener if the recognizer identifiesa cardiovascular problem. The heart sound being transmitted may becompressed to save transmission bandwidth.

In yet another aspect, a monitoring system for a person includes one ormore wireless nodes; and a wristwatch having a wireless transceiveradapted to communicate with the one or more wireless nodes; and anaccelerometer to detect a dangerous condition and to generate a warningwhen the dangerous condition is detected.

In yet another aspect, a monitoring system for a person includes one ormore wireless nodes forming a wireless mesh network; and a wearableappliance having a wireless transceiver adapted to communicate with theone or more wireless nodes; and a heartbeat detector coupled to thewireless transceiver. The system may also include an accelerometer todetect a dangerous condition such as a falling condition and to generatea warning when the dangerous condition is detected.

Implementations of the above aspect may include one or more of thefollowing. The wristwatch determines position based on triangulation.The wristwatch determines position based on RF signal strength and RFsignal angle. A switch detects a confirmatory signal from the person.The confirmatory signal includes a head movement, a hand movement, or amouth movement. The confirmatory signal includes the person's voice. Aprocessor in the system executes computer readable code to transmit ahelp request to a remote computer. The code can encrypt or scramble datafor privacy. The processor can execute voice over IP (VoIP) code toallow a user and a remote person to audibly communicate with each other.The voice communication system can include Zigbee VoIP or Bluetooth VoIPor 802.XX VoIP. The remote person can be a doctor, a nurse, a medicalassistant, or a caregiver. The system includes code to store and analyzepatient information. The patient information includes medicine takinghabits, eating and drinking habits, sleeping habits, or excise habits. Apatient interface is provided on a user computer for accessinginformation and the patient interface includes in one implementation atouch screen; voice-activated text reading; and one touch telephonedialing. The processor can execute code to store and analyze informationrelating to the person's ambulation. A global positioning system (GPS)receiver can be used to detect movement and where the person falls. Thesystem can include code to map the person's location onto an area forviewing. The system can include one or more cameras positioned tocapture three dimensional (3 D) video of the patient; and a servercoupled to the one or more cameras, the server executing code to detecta dangerous condition for the patient based on the 3 D video and allow aremote third party to view images of the patient when the dangerouscondition is detected. The system can also perform calibrating Dopplerradar signal with an actual blood pressure during a training phase todevelop a model and using the model with Doppler radar signal during anoperational phase to estimate continuous blood pressure.

In another aspect, a monitoring system for a person includes one or morewireless bases; and a cellular telephone having a wireless transceiveradapted to communicate with the one or more wireless bases; and anaccelerometer to detect a dangerous condition and to generate a warningwhen the dangerous condition is detected.

In yet another aspect, a monitoring system includes one or more camerasto determine a three dimensional (3 D) model of a person; means todetect a dangerous condition based on the 3 D model; and means togenerate a warning when the dangerous condition is detected.

In another aspect, a method to detect a dangerous condition for aninfant includes placing a pad with one or more sensors in the infant'sdiaper; collecting infant vital parameters; processing the vitalparameter to detect SIDS onset; and generating a warning.

In yet another embodiment, a wireless housing provides one or morebioelectric contacts conveniently positioned to collect bioelectricpatient data. The housing can be a patch, a wristwatch, a band, awristband, a chest band, a leg band, a sock, a glove, a foot pad, ahead-band, an ear-clip, an ear phone, a shower-cap, an armband, anear-ring, eye-glasses, sun-glasses, a belt, a sock, a shirt, a garment,a jewelry, a bed spread, a pillow cover, a pillow, a mattress, a blanketor a sleeping garment such as a pajama. The bed spread, pillow cover,pillow, mattress, blanket or pajama can have bioelectrically conductivecontacts in an array so that the patient can enjoy his/her sleep whilevital parameters can be captured. In one embodiment, an array ofparallel conductive lines can be formed on the housing side that facesthe patient and the electrical signal can be picked up. The datacaptured by the contacts are transmitted over the mesh network such asZigBee to a base station.

In the above embodiments, the base station can perform the bioelectricsignal processing to extract patient parameters from data captured bythe contacts. In this case, the base station may need a DSP or powerfulCPU to perform the calculations. Alternatively, in an ASP model, thebase station can simply compress the data and upload the data to acentral server or server farm for processing and the result of thesignal processing are sent back to the base station for relay to thepatient interface which can be a wrist-watch, a pad, or a band, amongothers, for notification of any warning signs.

Advantages of the system may include one or more of the following. Thesystem detects the warning signs of stroke and prompts the user to reacha health care provider within a few hours of symptom onset. The systemenables patent to properly manage acute stroke, and the resulting earlytreatment might reduce the degree of morbidity that is associated withfirst-ever strokes.

Other advantages of the invention may include one or more of thefollowing. The system for non-invasively and continually monitors asubject's arterial blood pressure, with reduced susceptibility to noiseand subject movement, and relative insensitivity to placement of theapparatus on the subject. The system does not need frequentrecalibration of the system while in use on the subject. The Dopplerdetection of heart rate and blood flow can be used to monitor patientswithout requiring them to wear or embed any medical device on the body.

In particular, it allows patients to conduct a low-cost, comprehensive,real-time monitoring of their blood pressure. Using the web servicessoftware interface, the invention then avails this information tohospitals, home-health care organizations, insurance companies,pharmaceutical agencies conducting clinical trials and otherorganizations. Information can be viewed using an Internet-basedwebsite, a personal computer, or simply by viewing a display on themonitor. Data measured several times each day provide a relativelycomprehensive data set compared to that measured during medicalappointments separated by several weeks or even months. This allows boththe patient and medical professional to observe trends in the data, suchas a gradual increase or decrease in blood pressure, which may indicatea medical condition. The invention also minimizes effects of white coatsyndrome since the monitor automatically makes measurements withbasically no discomfort; measurements are made at the patient's home orwork, rather than in a medical office.

In certain embodiments that supplement the Doppler radar with wearableappliance, such appliance is small, easily worn by the patient duringperiods of exercise or day-to-day activities, and non-invasivelymeasures blood pressure can be done in a matter of seconds withoutaffecting the patient. An on-board or remote processor can analyze thetime-dependent measurements to generate statistics on a patient's bloodpressure (e.g., average pressures, standard deviation, beat-to-beatpressure variations) that are not available with conventional devicesthat only measure systolic and diastolic blood pressure at isolatedtimes.

When used in conjunction with the Doppler radar, the wearable applianceprovides an in-depth, cost-effective mechanism to evaluate a patient'scardiac condition. Certain cardiac conditions can be controlled, and insome cases predicted, before they actually occur. Moreover, data fromthe patient can be collected and analyzed while the patient participatesin their normal, day-to-day activities.

In cases where the device has fall detection in addition to bloodpressure measurement, other advantages of the invention may include oneor more of the following. The system provides timely assistance andenables elderly and disabled individuals to live relatively independentlives. The system monitors physical activity patterns, detects theoccurrence of falls, and recognizes body motion patterns leading tofalls. Continuous monitoring of patients is done in an accurate,convenient, unobtrusive, private and socially acceptable manner since acomputer monitors the images and human involvement is allowed only underpre-designated events. The patient's privacy is preserved since humanaccess to videos of the patient is restricted: the system only allowshuman viewing under emergency or other highly controlled conditionsdesignated in advance by the user. When the patient is healthy, peoplecannot view the patient's video without the patient's consent. Only whenthe patient's safety is threatened would the system provide patientinformation to authorized medical providers to assist the patient. Whenan emergency occurs, images of the patient and related medical data canbe compiled and sent to paramedics or hospital for proper preparationfor pick up and check into emergency room.

The system allows certain designated people such as a family member, afriend, or a neighbor to informally check on the well-being of thepatient. The system is also effective in containing the spiraling costof healthcare and outpatient care as a treatment modality by providingremote diagnostic capability so that a remote healthcare provider (suchas a doctor, nurse, therapist or caregiver) can visually communicatewith the patient in performing remote diagnosis. The system allowsskilled doctors, nurses, physical therapists, and other scarce resourcesto assist patients in a highly efficient manner since they can do themajority of their functions remotely.

Additionally, a sudden change of activity (or inactivity) can indicate aproblem. The remote healthcare provider may receive alerts over theInternet or urgent notifications over the phone in case of such suddenaccident indicating changes. Reports of health/activity indicators andthe overall well being of the individual can be compiled for the remotehealthcare provider. Feedback reports can be sent to monitored subjects,their designated informal caregiver and their remote healthcareprovider. Feedback to the individual can encourage the individual toremain active. The content of the report may be tailored to the targetrecipient's needs, and can present the information in a formatunderstandable by an elder person unfamiliar with computers, via anappealing patient interface. The remote healthcare provider will haveaccess to the health and well-being status of their patients withoutbeing intrusive, having to call or visit to get such informationinterrogatively. Additionally, remote healthcare provider can receive areport on the health of the monitored subjects that will help themevaluate these individuals better during the short routine check upvisits. For example, the system can perform patient behavior analysissuch as eating/drinking/smoke habits and medication compliance, amongothers.

The patient's home equipment is simple to use and modular to allow forthe accommodation of the monitoring device to the specific needs of eachpatient. Moreover, the system is simple to install. Regular monitoringof the basic wellness parameters provides significant benefits inhelping to capture adverse events sooner, reduce hospital admissions,and improve the effectiveness of medications, hence, lowering patientcare costs and improving the overall quality of care. Suitable users forsuch systems are disease management companies, health insurancecompanies, self-insured employers, medical device manufacturers andpharmaceutical firms.

The system reduces costs by automating data collection and compliancemonitoring, and hence reduce the cost of nurses for hospital and nursinghome applications. At-home vital signs monitoring enables reducedhospital admissions and lower emergency room visits of chronic patients.Operators in the call centers or emergency response units get highquality information to identify patients that need urgent care so thatthey can be treated quickly, safely, and cost effectively. The Web basedtools allow easy access to patient information for authorized partiessuch as family members, neighbors, physicians, nurses, pharmacists,caregivers, and other affiliated parties to improved the Quality of Carefor the patient.

In an on-line pharmacy aspect, a method for providing patient access tomedication includes collecting patient medical information from apatient computer; securing the patient medical information and sendingthe secured patient medical information from the patient computer to aremote computer; remotely examining the patient and reviewing thepatient medical information; generating a prescription for the patientand sending the prescription to a pharmacy; and performing a druginteraction analysis on the prescription.

Implementations of the on-line pharmacy aspect may include one or moreof the following. The medical information can include temperature, EKG,blood pressure, weight, sugar level, image of the patient, or sound ofthe patient. Responses from the patient to a patient medicalquestionnaire can be captured. The doctor can listen to the patient'sorgan with a digital stethoscope, scanning a video of the patient,running a diagnostic test on the patient, verbally communicating withthe patient. The digital stethoscope can be a microphone orpiezoelectric transducer coupled to the Zigbee network to relay thesound. A plurality of medical rules can be applied to the medicalinformation to arrive at a diagnosis. Genetic tests or pharmacogenetictests can be run on the patient to check compatibility with theprescription. Approval for the prescription can come from one of: adoctor, a physician, a physician assistant, a nurse. The system canmonitor drug compliance, and can automatically ordering a medicationrefill from the pharmacy.

For pharmacy applications, advantages of the pharmacy system may includeone or more of the following. The system shares the patient's medicalhistory and can be updated by a remote physician and the remotedispensing pharmacy. As the doctor and the pharmacy have the same accessto the patient medical history database, patient data is updated in realtime, and is as current and complete as possible. The patient, doctor,pharmacy, and third party testing entities benefit from a uniformpricing structure that is based on the diagnosis and treatment. Thepatient only pays for standard medical treatments for his or herillness. The physician is paid a standard fee which covers the averagework spent with a patient with the specific type of medical situation.The dispensing pharmacy is able to provide the highest level of service,since it is able to double check all medications dispensed to eachpatient along with the optimal way to detect anticipated negative druginteractions. The pricing structure is competitive as physicians do notneed to be distributed physically, and those with specialty areas mayremain centrally located and yet be able to interact electronically withpatients. The system still provides physical access to specialists sincethe patients which are evaluated can be directed to visit a specialistsphysically, when remote review and contact is ineffectual. The on-linepharmacy tracks the specific needs and medical history of each patientand can provide an expert system to advise the patient on proper drugusage and potential drug interactions. The system automates thepurchasing of drugs, pricing the prescription or submission of theclaims to a third party for pricing, entering the complete prescriptionin their computer system, and auditing from third parties which providepayment. The on-line pharmacy provides detailed multimedia guidance orassistance to the patient regarding the filled prescription. The patientcan freely search for answers regarding the use of the filledprescription, its possible side effects, possible interactions withother drugs, possible alternative treatments, etc. The patient cancommunicate using video or VoIP with a remote pharmacist regarding anynumber of questions, and be counseled by the local pharmacist on the useof the filled prescription. Thus, the system minimizes the danger fromharmful side effects or drug interactions by providing patients withfull access to information. The system allows a patient to enjoy theselection and price of a mail-order pharmacy without subjecting thepatient to dangerous interactions or side effects which may occur inunsupervised prescription purchases. The on-line pharmacy offers theselection and benefits of a “central fill” pharmacy method withoutrequiring the local pharmacy to purchase drugs to fill eachprescription, price each prescription, or be subjected to audits fromthird parties who provide payment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for monitoring a person.

FIG. 2 illustrates an exemplary process for determining and gettingassistance for a patient or user.

FIG. 3 shows an exemplary network.

FIGS. 4A-4B show exemplary blood pressure determination processes.

FIGS. 4C-4E shows exemplary stroke determination processes.

DESCRIPTION

FIG. 1 shows an exemplary patient monitoring system. The system canoperate in a home, a nursing home, or a hospital. In this system, one ormore mesh network appliances 8 are provided to enable wirelesscommunication in the home monitoring system. Appliances 8 in the meshnetwork can include home security monitoring devices, door alarm, windowalarm, home temperature control devices, fire alarm devices, amongothers. Appliances 8 in the mesh network can be one of multiple portablephysiological transducer, such as a blood pressure monitor, heart ratemonitor, weight scale, thermometer, spirometer, single or multiple leadelectrocardiograph (ECG), a pulse oxymeter, a body fat monitor, acholesterol monitor, a signal from a medicine cabinet, a signal from adrug container, a signal from a commonly used appliance such as arefrigerator/stove/oven/washer, or a signal from an exercise machine onexercise parameters such as distance, time, and heart rate.

As will be discussed in more detail below, one appliance is a patientmonitoring device that can be worn by the patient and includes a singleor bi-directional wireless communication link, generally identified bythe bolt symbol in FIG. 1, for transmitting data from the appliances 8to the local hub or receiving station or base station server 20 by wayof a wireless radio frequency (RF) link using a proprietary ornon-proprietary protocol. For example, within a house, a user may havemesh network appliances that detect window and door contacts, smokedetectors and motion sensors, video cameras, key chain control,temperature monitors, CO and other gas detectors, vibration sensors, andothers. A user may have flood sensors and other detectors on a boat. Anindividual, such as an ill or elderly grandparent, may have access to apanic transmitter or other alarm transmitter. Other sensors and/ordetectors may also be included. The user may register these applianceson a central security network by entering the identification code foreach registered appliance/device and/or system. The mesh network can beZigbee network or 802.15 network. More details of the mesh network isshown in FIG. 3 and discussed in more detail below.

Another appliance can be a Doppler sensor that senses heart rate and/orEKG signals generated by the patient without requiring the patient towear anything. In this system, one or more wireless nodes form awireless mesh network; and each node can contain a wireless transceiverthat cooperate with other transceivers to form a Doppler radar to detectheart motion. From the heart motion data, an analyzer determines heartattack or stroke attack without requiring the patient to wearelectronics. The analyzer communicates with the wireless transceivers toreceive patient data over the wireless mesh network.

In one embodiment, two 802.X protocol LAN wireless adapters are used:one as a transmitter and the other one as a receiver. Antennas operateas separate transmit and receive antennas for performing transmissionand reception simultaneously during the process of wirelessly measuringheart beat. Each LAN wireless adapter can be an 802.16 (WiMAX), 802.15(ZigBee) or 802.11 (WiFi) adapter that can be wall mounted or placed onsuitable furniture.

Another embodiment uses a multiple input, multiple output (MIMO)wireless adapter chip set with four antennas and two radios such as theWiMAX MIMO transceiver UXA23465 from NXP with a suitable baseband ASICwhich send data over two transceivers simultaneously. The inventorcontemplates that the adapter can also be 802.11 (WiFi), 802.15 (Zigbee)or Bluetooth adapters.

The local oscillators of the adapters are synchronized by providing acommon crystal reference to the LO synthesizers in both cards. Thebaseband output of the receiver adapter is prefiltered with a low-passRC filter with a cut-off frequency of 100 Hz to remove out of band noiseand avoid aliasing error. The pre-filtered signal is digitized and usedto calculate heart rate. The digitized signal is the additionallyfiltered in the digital domain to separate the heart and breathingsignals. To determine heart rate, an autocorrelation function wascalculated for the heart signal. The periodicity of the autocorrelationfunction is used to determine the heart rate. A filter can also beapplied to extract breathing rate from the digitized signal.

In the Doppler radar phenomenon, the frequency of a radio signal isaltered when the signal reflects off of a moving object. The periodicmovement of the chest and internal organs modulates an incident ortransmitted radio signal from one of the wireless transceivers, and theresulting reflection is interpreted to deduce, for example, heart andbreathing activity. In one embodiment, the system uses WiMAXtransceivers which operate at higher frequencies, around 5-6 GHz andwhich provide high resolution and improved antenna patterns could beused for more detailed observations of arterial motion.

The non-invasive measuring techniques according to the present inventioncan be enhanced by the attachment of wireless sensors to criticallocations on the body. The body sensor technique allows the return orreflected signal to be more easily isolated from radar clutter effects,and provides a means for sensing additional data not easily derived froma radar signal, such as skin temperature. The body sensors can be assimple as conductive patches that attach to the skin and enhance thereflection of the incident radio signal at a particular location.Alternatively, the body sensors are more complex frequency resonantstructures, or even oscillating or multiplying semiconductor circuits.Such circuits can alter the reflected radio signal in time and/orfrequency, and can impose additional modulated data, which is generatedby, for example, skin temperature, bio-electric effects, re-radiatedradar effects, and physical acceleration.

A conducting surface will then reflect most of the energy from anincident radio wave. Placing such a surface or patch on a target area ofthe body, such as the chest or the skin over an artery, will enhance thereturn of the radar signal from that target area. As one skilled in theart will appreciate, if the physical dimensions of the conductingsurface are properly chosen, the path can act as an electricallyresonant antenna that provides an enhanced radar return.

In one embodiment, each person's heartbeat is a virtual fingerprint thatcan be used to identify one person from another person in the house. Asdiscussed above, suitable statistical recognizers such as Hidden MarkovModel (HMM) recognizers, neural network, fuzzy recognizer, dynamic timewarp (DTW) recognizer, a Bayesian network, or a Real Analytical ConstantModulus Algorithm (RACMA) recognizer, among others can be used todistinguish one person's heartbeat from another. This technique allowsthe system to track multiple people in a residence at once.Additionally, three or more transceivers can be positioned in theresidence so that their position can be determined throughtriangulation. The positional data, heart rate, and breathingrate/respiration rate, as well as change delta for each, can be datamined to determine the user's daily activity patterns. A Hidden MarkovModel (HMM) recognizer, a dynamic time warp (DTW) recognizer, a neuralnetwork, a fuzzy logic engine, or a Bayesian network can be applied tothe actual or the difference/change for a particular signal, for examplethe heart rate or breathing rate, to determine the likelihood of astroke attack in one embodiment. In another embodiment, the Dopplerradar picks up S1-S4 heart sounds as discussed below and determine thelikelihood of a stroke from the heart sound patterns for S1-S4.

A plurality of monitoring cameras 10 may be placed in variouspredetermined positions in a home of a patient 30. The cameras 10 can bewired or wireless. For example, the cameras can communicate overinfrared links or over radio links conforming to the 802X (e.g. 802.11A,802.11B, 802.11G, 802.15) standard or the Bluetooth standard to a basestation/server 20 may communicate over various communication links, suchas a direct connection, such a serial connection, USB connection,Firewire connection or may be optically based, such as infrared orwireless based, for example, home RF, IEEE standard 802.11a/b, Bluetoothor the like. In one embodiment, appliance 8 monitors the patient andactivates the camera 10 to capture and transmit video to an authorizedthird party for providing assistance should the appliance 8 detects thatthe user needs assistance or that an emergency had occurred.

The base station/server 20 stores the patient's ambulation pattern andvital parameters and can be accessed by the patient's family members(sons/daughters), physicians, caretakers, nurses, hospitals, and elderlycommunity. The base station/server 20 may communicate with the remoteserver 200 by DSL, T-1 connection over a private communication networkor a public information network, such as the Internet 100, among others.

Although the Doppler radar system can operate without requiring thepatient to wear the measuring devices thereon, the patient 30 may stillwear one or more wearable patient monitoring appliances such aswrist-watches or clip on devices or electronic jewelry to monitor thepatient. When used in conjunction with the Doppler radar, one wearableappliance such as a wrist-watch includes sensors 40, for example devicesfor sensing ECG, EKG, blood pressure, sugar level, among others. In oneembodiment, the sensors 40 are mounted on the patient's wrist (such as awristwatch sensor) and other convenient anatomical locations. Exemplarysensors 40 include standard medical diagnostics for detecting the body'selectrical signals emanating from muscles (EMG and EOG) and brain (EEG)and cardiovascular system (ECG). Leg sensors can include piezoelectricaccelerometers designed to give qualitative assessment of limb movement.Additionally, thoracic and abdominal bands used to measure expansion andcontraction of the thorax and abdomen respectively. A small sensor canbe mounted on the subject's finger in order to detect blood-oxygenlevels and pulse rate. Additionally, a microphone can be attached tothroat and used in sleep diagnostic recordings for detecting breathingand other noise. One or more position sensors can be used for detectingorientation of body (lying on left side, right side or back) duringsleep diagnostic recordings. Each of sensors 40 can individuallytransmit data to the server 20 using wired or wireless transmission.Alternatively, all sensors 40 can be fed through a common bus into asingle transceiver for wired or wireless transmission. The transmissioncan be done using a magnetic medium such as a floppy disk or a flashmemory card, or can be done using infrared or radio network link, amongothers. The sensor 40 can also include an indoor positioning system oralternatively a global position system (GPS) receiver that relays theposition and ambulatory patterns of the patient to the server 20 formobility tracking.

In one embodiment, the sensors 40 for monitoring vital signs areenclosed in a wrist-watch sized case supported on a wrist band. Thesensors can be attached to the back of the case. For example, in oneembodiment, Cygnus' AutoSensor (Redwood City, Calif.) is used as aglucose sensor. A low electric current pulls glucose through the skin.Glucose is accumulated in two gel collection discs in the AutoSensor.The AutoSensor measures the glucose and a reading is displayed by thewatch.

In another embodiment, EKG/ECG contact points are positioned on the backof a unit such as clothing, underwear or wearable electronics such as awrist-watch case. In the case of a garment, a piezoelectric transduceris placed on the back of the garment. As the user breathes, thepiezoelectric transducer is stretched and the resulting piezo output isused to detect breathing rate and heart rate, among others. In yetanother embodiment that provides continuous, beat-to-beat wrist arterialpulse rate measurements, a pressure sensor is housed in a wrist mountedcasing with a ‘free-floating’ plunger as the sensor applanates theradial artery. A strap provides a constant force for effectiveapplanation and ensuring the position of the sensor housing to remainconstant after any wrist movements. The change in the electrical signalsdue to change in pressure is detected as a result of the piezoresistivenature of the sensor are then analyzed to arrive at various arterialpressure, systolic pressure, diastolic pressure, time indices, and otherblood pressure parameters.

The case may be of a number of variations of shape but can beconveniently made a rectangular, approaching a box-like configuration.The wrist-band can be an expansion band or a wristwatch strap ofplastic, leather or woven material. The wrist-band further contains anantenna for transmitting or receiving radio frequency signals. Thewristband and the antenna inside the band are mechanically coupled tothe top and bottom sides of the wrist-watch housing. Further, theantenna is electrically coupled to a radio frequency transmitter andreceiver for wireless communications with another computer or anotheruser. Although a wrist-band is disclosed, a number of substitutes may beused, including a belt, a ring holder, a brace, or a bracelet, amongother suitable substitutes known to one skilled in the art. The housingcontains the processor and associated peripherals to provide thehuman-machine interface. A display is located on the front section ofthe housing. A speaker, a microphone, and a plurality of push-buttonswitches and are also located on the front section of housing. Aninfrared LED transmitter and an infrared LED receiver are positioned onthe right side of housing to enable the watch to communicate withanother computer using infrared transmission.

In another embodiment, the sensors 40 are mounted on the patient'sclothing. For example, sensors can be woven into a single-piece garment(an undershirt) on a weaving machine. A plastic optical fiber can beintegrated into the structure during the fabric production processwithout any discontinuities at the armhole or the seams. Aninterconnection technology transmits information from (and to) sensorsmounted at any location on the body thus creating a flexible “bus”structure. T-Connectors—similar to “button clips” used in clothing—areattached to the fibers that serve as a data bus to carry the informationfrom the sensors (e.g., EKG sensors) on the body. The sensors will pluginto these connectors and at the other end similar T-Connectors will beused to transmit the information to monitoring equipment or personalstatus monitor. Since shapes and sizes of humans will be different,sensors can be positioned on the right locations for all patients andwithout any constraints being imposed by the clothing. Moreover, theclothing can be laundered without any damage to the sensors themselves.In addition to the fiber optic and specialty fibers that serve assensors and data bus to carry sensory information from the wearer to themonitoring devices, sensors for monitoring the respiration rate can beintegrated into the structure.

In another embodiment, instead of being mounted on the patient, thesensors can be mounted on fixed surfaces such as walls or tables, forexample. One such sensor is a motion detector. Another sensor is aproximity sensor. The fixed sensors can operate alone or in conjunctionwith the cameras 10. In one embodiment where the motion detectoroperates with the cameras 10, the motion detector can be used to triggercamera recording. Thus, as long as motion is sensed, images from thecameras 10 are not saved. However, when motion is not detected, theimages are stored and an alarm may be generated. In another embodimentwhere the motion detector operates stand alone, when no motion issensed, the system generates an alarm.

The server 20 also executes one or more software modules to analyze datafrom the patient. A module 50 monitors the patient's vital signs such asECG/EKG and generates warnings should problems occur. In this module,vital signs can be collected and communicated to the server 20 usingwired or wireless transmitters. In one embodiment, the server 20 feedsthe data to a statistical analyzer such as a neural network which hasbeen trained to flag potentially dangerous conditions. The neuralnetwork can be a back-propagation neural network, for example. In thisembodiment, the statistical analyzer is trained with training data wherecertain signals are determined to be undesirable for the patient, givenhis age, weight, and physical limitations, among others. For example,the patient's glucose level should be within a well established range,and any value outside of this range is flagged by the statisticalanalyzer as a dangerous condition. As used herein, the dangerouscondition can be specified as an event or a pattern that can causephysiological or psychological damage to the patient. Moreover,interactions between different vital signals can be accounted for sothat the statistical analyzer can take into consideration instanceswhere individually the vital signs are acceptable, but in certaincombinations, the vital signs can indicate potentially dangerousconditions. Once trained, the data received by the server 20 can beappropriately scaled and processed by the statistical analyzer. Inaddition to statistical analyzers, the server 20 can process vital signsusing rule-based inference engines, fuzzy logic, as well as conventionalif-then logic. Additionally, the server can process vital signs usingHidden Markov Models (HMMs), dynamic time warping, or template matching,among others.

The system can also monitor the patient's gait pattern and generatewarnings should the patient's gait patterns indicate that the patient islikely to fall. The system will detect patient skeleton structure,stride and frequency; and based on this information to judge whetherpatient has joint problem, asymmetrical bone structure, among others.The system can store historical gait information, and by overlayingcurrent structure to the historical (normal) gait information, gaitchanges can be detected. In the camera embodiment, an estimate of thegait pattern is done using the camera. In a camera-less embodiment, thegait can be sensed by providing a sensor on the floor and a sensor nearthe head and the variance in the two sensor positions are used toestimate gait characteristics.

The system also provides a patient interface 90 to assist the patient ineasily accessing information. In one embodiment, the patient interfaceincludes a touch screen; voice-activated text reading; one touchtelephone dialing; and video conferencing. The touch screen has largeicons that are pre-selected to the patient's needs, such as his or herfavorite web sites or application programs. The voice activated textreading allows a user with poor eye-sight to get information from thepatient interface 90. Buttons with pre-designated dialing numbers, orvideo conferencing contact information allow the user to call a friendor a healthcare provider quickly.

In one embodiment, medicine for the patient is tracked using radiofrequency identification (RFID) tags. In this embodiment, each drugcontainer is tracked through an RFID tag that is also a drug label. TheRF tag is an integrated circuit that is coupled with a mini-antenna totransmit data. The circuit contains memory that stores theidentification Code and other pertinent data to be transmitted when thechip is activated or interrogated using radio energy from a reader. Areader consists of an RF antenna, transceiver and a micro-processor. Thetransceiver sends activation signals to and receives identification datafrom the tag. The antenna may be enclosed with the reader or locatedoutside the reader as a separate piece. RFID readers communicatedirectly with the RFID tags and send encrypted usage data over thepatient's network to the server 200 and eventually over the Internet100. The readers can be built directly into the walls or the cabinetdoors.

In one embodiment, capacitively coupled RFID tags are used. Thecapacitive RFID tag includes a silicon microprocessor that can store 96bits of information, including the pharmaceutical manufacturer, drugname, usage instruction and a 40-bit serial number. A conductive carbonink acts as the tag's antenna and is applied to a paper substratethrough conventional printing means. The silicon chip is attached toprinted carbon-ink electrodes on the back of a paper label, creating alow-cost, disposable tag that can be integrated on the drug label. Theinformation stored on the drug labels is written in a Medicine MarkupLanguage (MML), which is based on the eXtensible Markup Language (XML).MML would allow all computers to communicate with any computer system ina similar way that Web servers read Hyper Text Markup Language (HTML),the common language used to create Web pages.

After receiving the medicine container, the patient places the medicinein a medicine cabinet, which is also equipped with a tag reader. Thissmart cabinet then tracks all medicine stored in it. It can track themedicine taken, how often the medicine is restocked and can let thepatient know when a particular medication is about to expire. At thispoint, the server 200 can order these items automatically. The server200 also monitors drug compliance, and if the patient does not removethe bottle to dispense medication as prescribed, the server 200 sends awarning to the healthcare provider.

The database tracks typical arm and leg movements to determine whetherthe user is experiencing muscle weakness reflective of a stroke. Ifmuscle weakness is detected, the system presents the user withadditional tests to confirm the likelihood of a stroke attack. If theinformation indicates a stroke had occurred, the system stores the timeof the stroke detection and calls for emergency assistance to get timelytreatment for the stroke. The user's habits and movements can bedetermined by the system for stroke detection. This is done by trackinglocation, ambulatory travel vectors and time in a database. If the usertypically sleeps between 10 pm to 6 am, the location would reflect thatthe user's location maps to the bedroom between 10 pm and 6 am. In oneexemplary system, the system builds a schedule of the user's activity asfollows:

Location Time Start Time End Heart Rate Bed room 10 pm 6 am 60-80  Gymroom 6 am 7 am 90-120 Bath room 7 am 7:30 am 85-120 Dining room 7:30 am8:45 am 80-90  Home Office 8:45 am 11:30 am 85-100 . . . . . .

Other data such as EKG can be tracked, for example. The habit trackingis adaptive in that it gradually adjusts to the user's new habits. Ifthere are sudden changes, the system flags these sudden changes forfollow up. For instance, if the user spends three hours in the bathroom,the system prompts the third party (such as a call center) to follow upwith the patient to make sure he or she does not need help.

In one embodiment, data driven analyzers may be used to track thepatient's habits. These data driven analyzers may incorporate a numberof models such as parametric statistical models, non-parametricstatistical models, clustering models, nearest neighbor models,regression methods, and engineered (artificial) neural networks. Priorto operation, data driven analyzers or models of the patient's habits orambulation patterns are built using one or more training sessions. Thedata used to build the analyzer or model in these sessions are typicallyreferred to as training data. As data driven analyzers are developed byexamining only training examples, the selection of the training data cansignificantly affect the accuracy and the learning speed of the datadriven analyzer. One approach used heretofore generates a separate dataset referred to as a test set for training purposes. The test set isused to avoid overfitting the model or analyzer to the training data.Overfitting refers to the situation where the analyzer has memorized thetraining data so well that it fails to fit or categorize unseen data.Typically, during the construction of the analyzer or model, theanalyzer's performance is tested against the test set. The selection ofthe analyzer or model parameters is performed iteratively until theperformance of the analyzer in classifying the test set reaches anoptimal point. At this point, the training process is completed. Analternative to using an independent training and test set is to use amethodology called cross-validation. Cross-validation can be used todetermine parameter values for a parametric analyzer or model for anon-parametric analyzer. In cross-validation, a single training data setis selected. Next, a number of different analyzers or models are builtby presenting different parts of the training data as test sets to theanalyzers in an iterative process. The parameter or model structure isthen determined on the basis of the combined performance of all modelsor analyzers. Under the cross-validation approach, the analyzer or modelis typically retrained with data using the determined optimal modelstructure.

In general, multiple dimensions of a user's daily activities such asstart and stop times of interactions of different interactions areencoded as distinct dimensions in a database. A predictive model,including time series models such as those employing autoregressionanalysis and other standard time series methods, dynamic Bayesiannetworks and Continuous Time Bayesian Networks, or temporalBayesian-network representation and reasoning methodology, is built, andthen the model, in conjunction with a specific query makes targetinferences.

Bayesian networks provide not only a graphical, easily interpretablealternative language for expressing background knowledge, but they alsoprovide an inference mechanism; that is, the probability of arbitraryevents can be calculated from the model. Intuitively, given a Bayesiannetwork, the task of mining interesting unexpected patterns can berephrased as discovering item sets in the data which are much more—ormuch less—frequent than the background knowledge suggests. These casesare provided to a learning and inference subsystem, which constructs aBayesian network that is tailored for a target prediction. The Bayesiannetwork is used to build a cumulative distribution over events ofinterest.

In another embodiment, a genetic algorithm (GA) search technique can beused to find approximate solutions to identifying the user's habits.Genetic algorithms are a particular class of evolutionary algorithmsthat use techniques inspired by evolutionary biology such asinheritance, mutation, natural selection, and recombination (orcrossover). Genetic algorithms are typically implemented as a computersimulation in which a population of abstract representations (calledchromosomes) of candidate solutions (called individuals) to anoptimization problem evolves toward better solutions. Traditionally,solutions are represented in binary as strings of 0 s and 1 s, butdifferent encodings are also possible. The evolution starts from apopulation of completely random individuals and happens in generations.In each generation, the fitness of the whole population is evaluated,multiple individuals are stochastically selected from the currentpopulation (based on their fitness), modified (mutated or recombined) toform a new population, which becomes current in the next iteration ofthe algorithm.

Substantially any type of learning system or process may be employed todetermine the user's ambulatory and living patterns so that unusualevents can be flagged.

In one embodiment, clustering operations are performed to detectpatterns in the data. In another embodiment, a neural network is used torecognize each pattern as the neural network is quite robust atrecognizing user habits or patterns. Once the treatment features havebeen characterized, the neural network then compares the input userinformation with stored templates of treatment vocabulary known by theneural network recognizer, among others. The recognition models caninclude a Hidden Markov Model (HMM), a dynamic programming model, aneural network, a fuzzy logic, or a template matcher, among others.These models may be used singly or in combination.

Dynamic programming considers all possible points within the permitteddomain for each value of i. Because the best path from the current pointto the next point is independent of what happens beyond that point.Thus, the total cost of [i(k), j(k)] is the cost of the point itselfplus the cost of the minimum path to it. Preferably, the values of thepredecessors can be kept in an MxN array, and the accumulated cost keptin a 2xN array to contain the accumulated costs of the immediatelypreceding column and the current column. However, this method requiressignificant computing resources. For the recognizer to find the optimaltime alignment between a sequence of frames and a sequence of nodemodels, it must compare most frames against a plurality of node models.One method of reducing the amount of computation required for dynamicprogramming is to use pruning. Pruning terminates the dynamicprogramming of a given portion of user habit information against a giventreatment model if the partial probability score for that comparisondrops below a given threshold. This greatly reduces computation.

Considered to be a generalization of dynamic programming, a hiddenMarkov model is used in the preferred embodiment to evaluate theprobability of occurrence of a sequence of observations O(1), O(2), . .. O(t), . . . , O(T), where each observation O(t) may be either adiscrete symbol under the VQ approach or a continuous vector. Thesequence of observations may be modeled as a probabilistic function ofan underlying Markov chain having state transitions that are notdirectly observable. In one embodiment, the Markov network is used tomodel a number of user habits and activities. The transitions betweenstates are represented by a transition matrix A=[a(i,j)]. Each a(i,j)term of the transition matrix is the probability of making a transitionto state j given that the model is in state i. The output symbolprobability of the model is represented by a set of functions B=[b(j)(O(t)], where the b(j) (O(t) term of the output symbol matrix is theprobability of outputting observation O(t), given that the model is instate j. The first state is always constrained to be the initial statefor the first time frame of the utterance, as only a prescribed set ofleft to right state transitions are possible. A predetermined finalstate is defined from which transitions to other states cannot occur.Transitions are restricted to reentry of a state or entry to one of thenext two states. Such transitions are defined in the model as transitionprobabilities. Although the preferred embodiment restricts the flowgraphs to the present state or to the next two states, one skilled inthe art can build an HMM model without any transition restrictions,although the sum of all the probabilities of transitioning from anystate must still add up to one. In each state of the model, the currentfeature frame may be identified with one of a set of predefined outputsymbols or may be labeled probabilistically. In this case, the outputsymbol probability b(j) O(t) corresponds to the probability assigned bythe model that the feature frame symbol is O(t). The model arrangementis a matrix A=[a(i,j)] of transition probabilities and a technique ofcomputing B=b(j) O(t), the feature frame symbol probability in state j.The Markov model is formed for a reference pattern from a plurality ofsequences of training patterns and the output symbol probabilities aremultivariate Gaussian function probability densities. The patient habitinformation is processed by a feature extractor. During learning, theresulting feature vector series is processed by a parameter estimator,whose output is provided to the hidden Markov model. The hidden Markovmodel is used to derive a set of reference pattern templates, eachtemplate representative of an identified pattern in a vocabulary set ofreference treatment patterns. The Markov model reference templates arenext utilized to classify a sequence of observations into one of thereference patterns based on the probability of generating theobservations from each Markov model reference pattern template. Duringrecognition, the unknown pattern can then be identified as the referencepattern with the highest probability in the likelihood calculator. TheHMM template has a number of states, each having a discrete value.However, because treatment pattern features may have a dynamic patternin contrast to a single value. The addition of a neural network at thefront end of the HMM in an embodiment provides the capability ofrepresenting states with dynamic values. The input layer of the neuralnetwork comprises input neurons. The outputs of the input layer aredistributed to all neurons in the middle layer. Similarly, the outputsof the middle layer are distributed to all output states, which normallywould be the output layer of the neuron. However, each output hastransition probabilities to itself or to the next outputs, thus forminga modified HMM. Each state of the thus formed HMM is capable ofresponding to a particular dynamic signal, resulting in a more robustHMM. Alternatively, the neural network can be used alone withoutresorting to the transition probabilities of the HMM architecture.

The system allows patients to conduct a low-cost, comprehensive,real-time monitoring of their vital parameters such as ambulation andfalls. Information can be viewed using an Internet-based website, apersonal computer, or simply by viewing a display on the monitor. Datameasured several times each day provide a relatively comprehensive dataset compared to that measured during medical appointments separated byseveral weeks or even months. This allows both the patient and medicalprofessional to observe trends in the data, such as a gradual increaseor decrease in blood pressure, which may indicate a medical condition.The invention also minimizes effects of white coat syndrome since themonitor automatically makes measurements with basically no discomfort;measurements are made at the patient's home or work, rather than in amedical office.

When used in conjunction with the Doppler radar, the wearable applianceis small, easily worn by the patient during periods of exercise orday-to-day activities, and non-invasively measures blood pressure can bedone in a matter of seconds without affecting the patient. An on-boardor remote processor can analyze the time-dependent measurements togenerate statistics on a patient's blood pressure (e.g., averagepressures, standard deviation, beat-to-beat pressure variations) thatare not available with conventional devices that only measure systolicand diastolic blood pressure at isolated times.

The wearable appliance provides an in-depth, cost-effective mechanism toevaluate a patient's health condition. Certain cardiac conditions can becontrolled, and in some cases predicted, before they actually occur.Moreover, data from the patient can be collected and analyzed while thepatient participates in their normal, day-to-day activities.

Software programs associated with the Internet-accessible website,secondary software system, and the personal computer analyze the bloodpressure, and heart rate, and pulse oximetry values to characterize thepatient's cardiac condition. These programs, for example, may provide areport that features statistical analysis of these data to determineaverages, data displayed in a graphical format, trends, and comparisonsto doctor-recommended values.

When the appliance cannot communicate with the mesh network, theappliance simply stores information in memory and continues to makemeasurements. The watch component automatically transmits all the storedinformation (along with a time/date stamp) when it comes in proximity tothe wireless mesh network, which then transmits the information throughthe wireless network.

In one embodiment, the server provides a web services that communicatewith third party software through an interface. To generate vitalparameters such as blood pressure information for the web servicessoftware interface, the patient continuously wears the blood-pressuremonitor for a short period of time, e.g. one to two weeks after visitinga medical professional during a typical check up or after signing up fora short-term monitoring program through the website. In this case, thewearable device such as the watch measures mobility through theaccelerometer and blood pressure in a near-continuous, periodic mannersuch as every fifteen minutes. This information is then transmitted overthe mesh network to a base station that communicates over the Internetto the server.

To view information sent from the blood-pressure monitor and falldetector on the wearable appliance, the patient or an authorized thirdparty such as family members, emergency personnel, or medicalprofessional accesses a patient user interface hosted on the web server200 through the Internet 100 from a remote computer system. The patientinterface displays vital information such as ambulation, blood pressureand related data measured from a single patient. The system may alsoinclude a call center, typically staffed with medical professionals suchas doctors, nurses, or nurse practitioners, whom access a care-providerinterface hosted on the same website on the server 200. Thecare-provider interface displays vital data from multiple patients.

The wearable appliance has an indoor positioning system and processesthese signals to determine a location (e.g., latitude, longitude, andaltitude) of the monitor and, presumably, the patient. This locationcould be plotted on a map by the server, and used to locate a patientduring an emergency, e.g. to dispatch an ambulance.

In one embodiment, the web page hosted by the server 200 includes aheader field that lists general information about the patient (e.g.name, age, and ID number, general location, and information concerningrecent measurements); a table that lists recently measured bloodpressure data and suggested (i.e. doctor-recommended) values of thesedata; and graphs that plot the systolic and diastolic blood pressuredata in a time-dependent manner. The header field additionally includesa series of tabs that each link to separate web pages that include,e.g., tables and graphs corresponding to a different data measured bythe wearable device such as calorie consumption/dissipation, ambulationpattern, sleeping pattern, heart rate, pulse oximetry, and temperature.The table lists a series of data fields that show running average valuesof the patient's daily, monthly, and yearly vital parameters. The levelsare compared to a series of corresponding suggested values of vitalparameters that are extracted from a database associated with the website. The suggested values depend on, among other things, the patient'sage, sex, and weight. The table then calculates the difference betweenthe running average and suggested values to give the patient an idea ofhow their data compares to that of a healthy patient. The web softwareinterface may also include security measures such as authentication,authorization, encryption, credential presentation, and digitalsignature resolution. The interface may also be modified to conform toindustry-mandated, XML schema definitions, while being backwardscompatible with any existing XML schema definitions.

The system provides for self-registration of appliances by the user.Data can be synchronized between the Repository and appliance(s) via thebase station 20. The user can preview the readings received from theappliance(s) and reject erroneous readings. The user or treatingprofessional can set up the system to generate alerts against receiveddata, based on pre-defined parameters. The system can determine trendsin received data, based on user defined parameters.

Appliance registration is the process by which a patient monitoringappliance is associated with one or more users of the system. Thismechanism is also used when provisioning appliances for a user by athird party, such as a clinician (or their respective delegate). In oneimplementation, the user (or delegate) logs into the portal to selectone or more appliances and available for registration. In turn, the basestation server 20 broadcasts a query to all nodes in the mesh network toretrieve identification information for the appliance such asmanufacturer information, appliance model information, appliance serialnumber and optionally a hub number (available on hub packaging). Theuser may register more than one appliance at this point. The systemoptionally sets up a service subscription for appliance(s) usage. Thisincludes selecting service plans and providing payment information. Theappliance(s) are then associated with this user's account and a controlfile with appliance identification information is synchronized betweenthe server 200 and the base station 20 and each appliance oninitialization. In one embodiment, each appliance 8 transmits data tothe base station 20 in an XML format for ease of interfacing and iseither kept encrypted or in a non-readable format on the base station 20for security reasons.

The base station 20 frequently collects and synchronizes data from theappliances 8. The base station 20 may use one of various transportationmethods to connect to the repository on the server 200 using a PC asconduit or through a connection established using an embedded modem(connected to a phone line), a wireless router (DSL or cable wirelessrouter), a cellular modem, or another network-connected appliance (suchas, but not limited to, a web-phone, video-phone, embedded computer, PDAor handheld computer).

In one embodiment, users may set up alerts or reminders that aretriggered when one or more reading meet a certain set of conditions,depending on parameters defined by the user. The user chooses thecondition that they would like to be alerted to and by providing theparameters (e.g. threshold value for the reading) for alert generation.Each alert may have an interval which may be either the number of datapoints or a time duration in units such as hours, days, weeks or months.The user chooses the destination where the alert may be sent. Thisdestination may include the user's portal, e-mail, pager, voice-mail orany combination of the above.

Trends are determined by applying mathematical and statistical rules(e.g. moving average and deviation) over a set of reading values. Eachrule is configurable by parameters that are either automaticallycalculated or are set by the user.

The user may give permission to others as needed to read or edit theirpersonal data or receive alerts. The user or clinician could have a listof people that they want to monitor and have it show on their “MyAccount” page, which serves as a local central monitoring station in oneembodiment. Each person may be assigned different access rights whichmay be more or less than the access rights that the patient has. Forexample, a doctor or clinician could be allowed to edit data for exampleto annotate it, while the patient would have read-only privileges forcertain pages. An authorized person could set the reminders and alertsparameters with limited access to others. In one embodiment, the basestation server 20 serves a web page customized by the user or the user'srepresentative as the monitoring center that third parties such asfamily, physicians, or caregivers can log in and access information. Inanother embodiment, the base station 20 communicates with the server 200at a call center so that the call center provides all services. In yetanother embodiment, a hybrid solution where authorized representativescan log in to the base station server 20 access patient informationwhile the call center logs into both the server 200 and the base stationserver 20 to provide complete care services to the patient.

The server 200 may communicate with a business process outsourcing (BPO)company or a call center to provide central monitoring in an environmentwhere a small number of monitoring agents can cost effectively monitormultiple people 24 hours a day. A call center agent, a clinician or anursing home manager may monitor a group or a number of users via asummary “dashboard” of their readings data, with ability to drill-downinto details for the collected data. A clinician administrator maymonitor the data for and otherwise administer a number of users of thesystem. A summary “dashboard” of readings from all Patients assigned tothe Administrator is displayed upon log in to the Portal by theAdministrator. Readings may be color coded to visually distinguishnormal vs. readings that have generated an alert, along with descriptionof the alert generated. The Administrator may drill down into thedetails for each Patient to further examine the readings data, viewcharts etc. in a manner similar to the Patient's own use of the system.The Administrator may also view a summary of all the appliancesregistered to all assigned Patients, including but not limited to allappliance identification information. The Administrator has access onlyto information about Patients that have been assigned to theAdministrator by a Super Administrator. This allows for segmenting theentire population of monitored Patients amongst multiple Administrators.The Super Administrator may assign, remove and/or reassign Patientsamongst a number of Administrators.

In one embodiment, a patient using an Internet-accessible computer andweb browser, directs the browser to an appropriate URL and signs up fora service for a short-term (e.g., 1 month) period of time. The companyproviding the service completes an accompanying financial transaction(e.g. processes a credit card), registers the patient, and ships thepatient a wearable appliance for the short period of time. Theregistration process involves recording the patient's name and contactinformation, a number associated with the monitor (e.g. a serialnumber), and setting up a personalized website. The patient then usesthe monitor throughout the monitoring period, e.g. while working,sleeping, and exercising. During this time the monitor measures datafrom the patient and wirelessly transmits it through the channel to adata center. There, the data are analyzed using software running oncomputer servers to generate a statistical report. The computer serversthen automatically send the report to the patient using email, regularmail, or a facsimile machine at different times during the monitoringperiod. When the monitoring period is expired, the patient ships thewearable appliance back to the monitoring company.

Different web pages may be designed and accessed depending on theend-user. As described above, individual users have access to web pagesthat only their ambulation and blood pressure data (i.e., the patientinterface), while organizations that support a large number of patients(nursing homes or hospitals) have access to web pages that contain datafrom a group of patients using a care-provider interface. Otherinterfaces can also be used with the web site, such as interfaces usedfor: insurance companies, members of a particular company, clinicaltrials for pharmaceutical companies, and e-commerce purposes. Vitalpatient data displayed on these web pages, for example, can be sortedand analyzed depending on the patient's medical history, age, sex,medical condition, and geographic location. The web pages also support awide range of algorithms that can be used to analyze data once they areextracted from the data packets. For example, an instant message oremail can be sent out as an alert in response to blood pressureindicating a medical condition that requires immediate attention.Alternatively, the message could be sent out when a data parameter (e.g.systolic blood pressure) exceeds a predetermined value. In some cases,multiple parameters (e.g., fall detection, positioning data, and bloodpressure) can be analyzed simultaneously to generate an alert message.In general, an alert message can be sent out after analyzing one or moredata parameters using any type of algorithm. These algorithms range fromthe relatively simple (e.g., comparing blood pressure to a recommendedvalue) to the complex (e.g., predictive medical diagnoses using datamining techniques). In some cases data may be fit using algorithms suchas a linear or non-linear least-squares fitting algorithm.

In one embodiment, a physician, other health care practitioner, oremergency personnel is provided with access to patient medicalinformation through the server 200. In one embodiment, if the wearableappliance detects that the patient needs help, or if the patient decideshelp is needed, the system can call his or her primary care physician.If the patient is unable to access his or her primary care physician (oranother practicing physician providing care to the patient) a call fromthe patient is received, by an answering service or a call centerassociated with the patient or with the practicing physician. The callcenter determines whether the patient is exhibiting symptoms of anemergency condition by polling vital patient information generated bythe wearable device, and if so, the answering service contacts 911emergency service or some other emergency service. The call center canreview falls information, blood pressure information, and other vitalinformation to determine if the patient is in need of emergencyassistance. If it is determined that the patient in not exhibitingsymptoms of an emergent condition, the answering service may thendetermine if the patient is exhibiting symptoms of a non-urgentcondition. If the patient is exhibiting symptoms of a non-urgentcondition, the answering service will inform the patient that he or shemay log into the server 200 for immediate information on treatment ofthe condition. If the answering service determines that the patient isexhibiting symptoms that are not related to a non-urgent condition, theanswering service may refer the patient to an emergency room, a clinic,the practicing physician (when the practicing physician is available)for treatment.

In another embodiment, the wearable appliance permits direct access tothe call center when the user pushes a switch or button on theappliance, for instance. In one implementation, telephones and switchingsystems in call centers are integrated with the home mesh network toprovide for, among other things, better routing of telephone calls,faster delivery of telephone calls and associated information, andimproved service with regard to client satisfaction throughcomputer-telephony integration (CTI). CTI implementations of variousdesign and purpose are implemented both within individual call-centersand, in some cases, at the telephone network level. For example,processors running CTI software applications may be linked to telephoneswitches, service control points (SCPs), and network entry points withina public or private telephone network. At the call-center level,CTI-enhanced processors, data servers, transaction servers, and thelike, are linked to telephone switches and, in some cases, to similarCTI hardware at the network level, often by a dedicated digital link.CTI processors and other hardware within a call-center is commonlyreferred to as customer premises equipment (CPE). It is the CTIprocessor and application software is such centers that providescomputer enhancement to a call center. In a CTI-enhanced call center,telephones at agent stations are connected to a central telephonyswitching apparatus, such as an automatic call distributor (ACD) switchor a private branch exchange (PBX). The agent stations may also beequipped with computer terminals such as personal computer/video displayunit's (PC/VDU's) so that agents manning such stations may have accessto stored data as well as being linked to incoming callers by telephoneequipment. Such stations may be interconnected through the PC/VDUs by alocal area network (LAN). One or more data or transaction servers mayalso be connected to the LAN that interconnects agent stations. The LANis, in turn, typically connected to the CTI processor, which isconnected to the call switching apparatus of the call center.

When a call from a patient arrives at a call center, whether or not thecall has been pre-processed at an SCP, the telephone number of thecalling line and the medical record are made available to the receivingswitch at the call center by the network provider. This service isavailable by most networks as caller-ID information in one of severalformats such as Automatic Number Identification (ANI). Typically thenumber called is also available through a service such as Dialed NumberIdentification Service (DNIS). If the call center is computer-enhanced(CTI), the phone number of the calling party may be used as a key toaccess additional medical and/or historical information from a customerinformation system (CIS) database at a server on the network thatconnects the agent workstations. In this manner information pertinent toa call may be provided to an agent, often as a screen pop on the agent'sPC/VDU.

The call center enables any of a first plurality of physician or healthcare practitioner terminals to be in audio communication over thenetwork with any of a second plurality of patient wearable appliances.The call center will route the call to a physician or other health carepractitioner at a physician or health care practitioner terminal andinformation related to the patient (such as an electronic medicalrecord) will be received at the physician or health care practitionerterminal via the network. The information may be forwarded via acomputer or database in the practicing physician's office or by acomputer or database associated with the practicing physician, a healthcare management system or other health care facility or an insuranceprovider. The physician or health care practitioner is then permitted toassess the patient, to treat the patient accordingly, and to forwardupdated information related to the patient (such as examination,treatment and prescription details related to the patient's visit to thepatient terminal) to the practicing physician via the network 200.

In one embodiment, the system informs a patient of a practicingphysician of the availability of the web services and referring thepatient to the web site upon agreement of the patient. A call from thepatient is received at a call center. The call center enables physiciansto be in audio communication over the network with any patient wearableappliances, and the call is routed to an available physician at one ofthe physician so that the available physician may carry on a two-wayconversation with the patient. The available physician is permitted tomake an assessment of the patient and to treat the patient. The systemcan forward information related to the patient to a health caremanagement system associated with the physician. The health caremanagement system may be a healthcare management organization, a pointof service health care system, or a preferred provider organization. Thehealth care practitioner may be a nurse practitioner or an internist.

The available health care practitioner can make an assessment of thepatient and to conduct an examination of the patient over the network,including optionally by a visual study of the patient. The system canmake an assessment in accordance with a protocol. The assessment can bemade in accordance with a protocol stored in a database and/or making anassessment in accordance with the protocol may include displaying inreal time a relevant segment of the protocol to the available physician.Similarly, permitting the physician to prescribe a treatment may includepermitting the physician to refer the patient to a third party fortreatment and/or referring the patient to a third party for treatmentmay include referring the patient to one or more of a primary carephysician, specialist, hospital, emergency room, ambulance service orclinic. Referring the patient to a third party may additionally includecommunicating with the third party via an electronic link included in arelevant segment of a protocol stored in a protocol database resident ona digital storage medium and the electronic link may be a hypertextlink. When a treatment is being prescribed by a physician, the systemcan communicate a prescription over the network to a pharmacy and/orcommunicating the prescription over the network to the pharmacy mayinclude communicating to the pharmacy instructions to be given to thepatient pertaining to the treatment of the patient. Communicating theprescription over the network to the pharmacy may also includecommunicating the prescription to the pharmacy via a hypertext linkincluded in a relevant segment of a protocol stored in a databaseresident on a digital storage medium. In accordance with another relatedembodiment, permitting the physician to conduct the examination may beaccomplished under conditions such that the examination is conductedwithout medical instruments at the patient terminal where the patient islocated.

In another embodiment, a system for delivering medical examination,diagnosis, and treatment services from a physician to a patient over anetwork includes a first plurality of health care practitioners at aplurality of terminals, each of the first plurality of health carepractitioner terminals including a display device that shows informationcollected by the wearable appliances and a second plurality of patientterminals or wearable appliances in audiovisual communication over anetwork with any of the first plurality of health care practitionerterminals. A call center is in communication with the patient wearableappliances and the health care practitioner terminals, the call centerrouting a call from a patient at one of the patient terminals to anavailable health care practitioner at one of the health carepractitioner terminals, so that the available health care practitionermay carry on a two-way conversation with the patient. A protocoldatabase resident on a digital storage medium is accessible to each ofthe health care practitioner terminals. The protocol database contains aplurality of protocol segments such that a relevant segment of theprotocol may be displayed in real time on the display device of thehealth care practitioner terminal of the available health carepractitioner for use by the available health care practitioner in makingan assessment of the patient. The relevant segment of the protocoldisplayed in real time on the display device of the health carepractitioner terminal may include an electronic link that establishescommunication between the available health care practitioner and a thirdparty and the third party may be one or more of a primary carephysician, specialist, hospital, emergency room, ambulance service,clinic or pharmacy.

In accordance with other related embodiment, the patient wearableappliance may include establish a direct connection to the call centerby pushing a button on the appliance. Further, the protocol database maybe resident on a server that is in communication with each of the healthcare practitioner terminals and each of the health care practitionerterminals may include a local storage device and the protocol databaseis replicated on the local storage device of one or more of thephysician terminals.

In another embodiment, a system for delivering medical examination,diagnosis, and treatment services from a physician to a patient over anetwork includes a first plurality of health care practitionerterminals, each of the first plurality of health care practitionerterminals including a display device and a second plurality of patientterminals in audiovisual communication over a network with any of thefirst plurality of health care practitioner terminals. Each of thesecond plurality of patient terminals includes a camera having pan, tiltand zoom modes, such modes being controlled from the first plurality ofhealth care practitioner terminals. A call center is in communicationwith the patient terminals and the health care practitioner terminalsand the call center routes a call from a patient at one of the patientterminals to an available health care practitioner at one of the healthcare practitioner terminals, so that the available health carepractitioner may carry on a two-way conversation with the patient andvisually observe the patient.

In one embodiment, the information is store in a secure environment,with security levels equal to those of online banking, social securitynumber input, and other confidential information. Conforming to HealthInsurance Portability and Accountability Act (HIPAA) requirements, thesystem creates audit trails, requires logins and passwords, and providesdata encryption to ensure the patient information is private and secure.The HIPAA privacy regulations ensure a national floor of privacyprotections for patients by limiting the ways that health plans,pharmacies, hospitals and other covered entities can use patients'personal medical information. The regulations protect medical recordsand other individually identifiable health information, whether it is onpaper, in computers or communicated orally.

FIG. 2 shows an exemplary process to monitor patient. First the processsets up a wireless network with one transmitter and one receiver (1000).The process then determines chest movement using Doppler principle toinfer vital signs such as heart activity (1002). The process thendetermines patient movement using the Doppler technique (1004). Sharpaccelerations may be used to indicate fall. The system determines vitalparameter including patient heart rate (1006). The system determines ifpatient needs assistance based on in-door position, fall detection andvital parameter (1008). If a fall is suspected, the system confirms thefall by communicating with the patient prior to calling a third partysuch as the patient's physician, nurse, family member, 911 call, 511call, 411 call, or a paid call center to get assistance for the patient(1010). If confirmed or if the patient is non-responsive, the systemcontacts the third party and sends voice over mesh network to applianceon the patient to allow one or more third parties to talk with thepatient (1012). If needed, the system calls and/or conferences emergencypersonnel into the call (1014).

FIG. 3 shows an exemplary network. Data collected and communicated onthe display 1382 as well as voice is transmitted to a base station 1390for communicating over a network to an authorized party 1394. The watchdisplay 1382 and the base station is part of a mesh network that maycommunicate with a medicine cabinet to detect opening or to eachmedicine container 1391 to detect medication compliance. Other devicesinclude mesh network thermometers, scales, or exercise devices. The meshnetwork also includes a plurality of home/room appliances 1392-1399. Theability to transmit voice is useful in the case the patient has fallendown and cannot walk to the base station 1390 to request help. Hence, inone embodiment, the watch captures voice from the user and transmits thevoice over the Zigbee mesh network to the base station 1390. The basestation 1390 in turn dials out to an authorized third party to allowvoice communication and at the same time transmits the collected patientvital parameter data and identifying information so that help can bedispatched quickly, efficiently and error-free. In one embodiment, thebase station 1390 is a POTS telephone base station connected to thewired phone network. In a second embodiment, the base station 1390 canbe a cellular telephone connected to a cellular network for voice anddata transmission. In a third embodiment, the base station 1390 can be aWiMAX or 802.16 standard base station that can communicate VoIP and dataover a wide area network. In one implementation, Zigbee or 802.15appliances communicate locally and then transmits to the wide areanetwork (WAN) such as the Internet over WiFi or WiMAX. Alternatively,the base station can communicate with the WAN over POTS and a wirelessnetwork such as cellular or WiMAX or both.

In one embodiment, the patient wears a garment fitted with piezoelectricdevices that can generate power for a vital sign sensor such as an EKGsensor. The vibration energy harvester consists of three main parts. Apiezoelectric transducer (PZT) serves as the energy conversion device, aspecialized power converter rectifies the resulting voltage, and acapacitor or battery stores the power. The PZT takes the form of analuminum cantilever with a piezoelectric patch. The vibration-inducedstrain in the PZT produces an ac voltage. The system repeatedly chargesa battery or capacitor, which then operates the EKG/EMG sensors or othersensors at a relatively low duty cycle. In one embodiment, a vest madeof piezoelectric materials can be wrapped around a person's chest togenerate power when strained through breathing as breathing increasesthe circumference of the chest for an average human by about 2.5 to 5cm. Energy can be constantly harvested because breathing is a constantactivity, even when a person is sedate. The amount of energy capturedthrough breathing indicates the breathing activity of the patient inaddition to powering or charging the circuit. The energy is convertedand stored in a low-leakage charge circuit until a predeterminedthreshold voltage is reached. Once the threshold is reached, theregulated power is allowed to flow for a sufficient period to power thewireless node such as the Zigbee CPU/transceiver. The transmission isdetected by nearby wireless nodes that are AC-powered and forwarded tothe base station for signal processing. Power comes from the vibrationof the system being monitored and the unit requires no maintenance, thusreducing life-cycle costs. In one embodiment, the housing of the unitcan be PZT composite, thus reducing the weight.

In another embodiment, body energy generation systems include electroactive polymers (EAPs) and dielectric elastomers. EAPs are a class ofactive materials that have a mechanical response to electricalstimulation and produce an electric potential in response to mechanicalstimulation. EAPs are divided into two categories, electronic, driven byelectric field, and ionic, driven by diffusion of ions. In oneembodiment, ionic polymers are used as biological actuators that assistmuscles for organs such as the heart and eyes. Since the ionic polymersrequire a solvent, the hydrated human body provides a naturalenvironment. Polymers are actuated to contract, assisting the heart topump, or correcting the shape of the eye to improve vision. Another useis as miniature surgical tools that can be inserted inside the body.EAPs can also be used as artificial smooth muscles, one of the originalideas for EAPs. These muscles could be placed in exoskeletal suits forsoldiers or prosthetic devices for disabled persons. Along with theenergy generation device, ionic polymers can be the energy storagevessel for harvesting energy. The capacitive characteristics of the EAPallow the polymers to be used in place of a standard capacitor bank.With EAP based jacket, when a person moves his/her arms, it will put theelectro active material around the elbow in tension to generate power.Dielectric elastomers can support 50-100% area strain and generate powerwhen compressed. Although the material could again be used in a bendingarm type application, a shoe type electric generator can be deployed byplacing the dielectric elastomers in the sole of a shoe. The constantcompressive force provided by the feet while walking would ensureadequate power generation.

In another embodiment, the wireless node can be powered from thermaland/or kinetic energy. Temperature differentials between oppositesegments of a conducting material result in heat flow and consequentlycharge flow since mobile, high-energy carriers diffuse from high to lowconcentration regions. Thermopiles consisting of n- and p-type materialselectrically joined at the high-temperature junction are thereforeconstructed, allowing heat flow to carry the dominant charge carriers ofeach material to the low temperature end, establishing in the process avoltage difference across the base electrodes. The generated voltage andpower is proportional to the temperature differential and the Seebeckcoefficient of the thermoelectric materials. Body heat from a user'swrist is captured by a thermoelectric element whose output is boostedand used to charge the a lithium ion rechargeable battery. The unitutilizes the Seeback Effect which describes the voltage created when atemperature difference exists across two different metals. Thethermoelectric generator takes body heat and dissipates it to theambient air, creating electricity as well as sensing body temperature inthe process.

In another embodiment, the kinetic energy of a person's movement isconverted into energy. As a person moves their weight, a small weightinside the wireless node moves like a pendulum and turns a magnet toproduce electricity which can be stored in a super-capacitor or arechargeable lithium battery. Similarly, in a vibration energyembodiment, energy extraction from vibrations is based on the movementof a “spring-mounted” mass relative to its support frame. Mechanicalacceleration is produced by vibrations that in turn cause the masscomponent to move and oscillate (kinetic energy). This relativedisplacement causes opposing frictional and damping forces to be exertedagainst the mass, thereby reducing and eventually extinguishing theoscillations. The damping forces literally absorb the kinetic energy ofthe initial vibration. This energy can be converted into electricalenergy via an electric field (electrostatic), magnetic field(electromagnetic), or strain on a piezoelectric material. The kineticenergy can be used to measure the activity of the patient whileproviding energy or charging the circuits at the same time.

Another embodiment extracts energy from the surrounding environmentusing a small rectanna (microwave-power receivers or ultrasound powerreceivers) placed in patches or membranes on the skin or alternativelyinjected underneath the skin. The rectanna converts the received emittedpower back to usable low frequency/dc power. A basic rectanna consistsof an antenna, a low pass filter, an ac/dc converter and a dc bypassfilter. The rectanna can capture renewable electromagnetic energyavailable in the radio frequency (RF) bands such as AM radio, FM radio,TV, very high frequency (VHF), ultra high frequency (UHF), global systemfor mobile communications (GSM), digital cellular systems (DCS) andespecially the personal communication system (PCS) bands, and unlicensedISM bands such as 2.4 GHz and 5.8 GHz bands, among others. The systemcaptures the ubiquitous electromagnetic energy (ambient RF noise andsignals) opportunistically present in the environment and transformingthat energy into useful electrical power. The energy-harvesting antennais preferably designed to be a wideband, omnidirectional antenna orantenna array that has maximum efficiency at selected bands offrequencies containing the highest energy levels. In a system with anarray of antennas, each antenna in the array can be designed to havemaximum efficiency at the same or different bands of frequency from oneanother. The collected RF energy is then converted into usable DC powerusing a diode-type or other suitable rectifier. This power may be usedto drive, for example, an amplifier/filter module connected to a secondantenna system that is optimized for a particular frequency andapplication. One antenna system can act as an energy harvester while theother antenna acts as a signal transmitter/receiver. The antenna circuitelements are formed using standard wafer manufacturing techniques. Theantenna output is stepped up and rectified before presented to a tricklecharger. The charger can recharge a complete battery by providing alarger potential difference between terminals and more power forcharging during a period of time. If battery includes individualmicro-battery cells, the trickle charger provides smaller amounts ofpower to each individual battery cell, with the charging proceeding on acell by cell basis. Charging of the battery cells continues wheneverambient power is available. As the load depletes cells, depleted cellsare switched out with charged cells. The rotation of depleted cells andcharged cells continues as required. Energy is banked and managed on amicro-cell basis.

In one embodiment to monitor heart failure, the Doppler radar detectsthe presence or absence, or rate of change, or heart activity. A normal,healthy, heart beats at a regular rate. Irregular heart beats, known ascardiac arrhythmia, on the other hand, may characterize an unhealthycondition. Another unhealthy condition is known as congestive heartfailure (“CHF”). CHF, also known as heart failure, is a condition wherethe heart has inadequate capacity to pump sufficient blood to meetmetabolic demand. CHF may be caused by a variety of sources, including,coronary artery disease, myocardial infarction, high blood pressure,heart valve disease, cardiomyopathy, congenital heart disease,endocarditis, myocarditis, and others. Unhealthy heart conditions may betreated using a cardiac rhythm management (CRM) system. Examples of CRMsystems, or pulse generator systems, include defibrillators (includingimplantable cardioverter defibrillator), pacemakers and other cardiacresynchronization devices.

The motion sensing capability of the Doppler radar can be used toprovide reproducible measurements. Body activity will increase cardiacoutput and also change the amount of blood in the systemic venous systemor lungs. Measurements of congestion may be most reproducible when bodyactivity is at a minimum and the patient is at rest. The use of anaccelerometer allows one to sense both body position and body activity.Comparative measurements over time may best be taken under reproducibleconditions of body position and activity. Ideally, measurements for theupright position should be compared as among themselves. Likewisemeasurements in the supine, prone, left lateral decubitus and rightlateral decubitus should be compared as among themselves. Othervariables can be used to permit reproducible measurements, i.e.variations of the cardiac cycle and variations in the respiratory cycle.The ventricles are at their most compliant during diastole. The end ofthe diastolic period is marked by the QRS on the electrocardiographicmeans (EKG) for monitoring the cardiac cycle. The second variable isrespiratory variation in impedance, which is used to monitor respiratoryrate and volume. As the lungs fill with air during inspiration,impedance increases, and during expiration, impedance decreases.Impedance can be measured during expiration to minimize the effect ofbreathing on central systemic venous volume. While respiration and CHFboth cause variations in impedance, the rates and magnitudes of theimpedance variation are different enough to separate out the respiratoryvariations which have a frequency of about 8 to 60 cycles per minute andcongestion changes which take at least several minutes to hours or evendays to occur. Also, the magnitude of impedance change is likely to bemuch greater for congestive changes than for normal respiratoryvariation. Thus, the system can detect congestive heart failure (CHF) inearly stages and alert a patient to prevent disabling and even lethalepisodes of CHF. Early treatment can avert progression of the disorderto a dangerous stage.

Various physiological parameters of medical and research interest may beextracted from repetitive measurements of the areas of variouscross-sections of the body. For example, pulmonary function parameters,such as respiration volumes and rates and apneas and their types, may bedetermined from measurements of, at least, a chest transversecross-sectional area and also an abdominal transverse cross-sectionalarea. Cardiac parameters, such central venous pressure, left and rightventricular volumes waveforms, and aortic and carotid artery pressurewaveforms, may be extracted from repetitive measurements of transversecross-sectional areas of the neck and of the chest passing through theheart. From the cardiac-related signals, indications of ischemia may beobtained independently of any ECG changes. Ventricular wall ischemia isknown to result in paradoxical wall motion during ventricularcontraction (the ischemic segment paradoxically “balloons” outwardinstead of normally contracting inward). Such paradoxical wall motion,and thus indications of cardiac ischemia, may be extracted from chesttransverse cross-section area measurements. Left or right ventricularischemia may be distinguished where paradoxical motion is seenpredominantly in left or right ventricular waveforms, respectively. Foranother example, observations of the onset of contraction in the leftand right ventricles separately may be of use in providing feedback tobi-ventricular cardiac pacing devices. For a further example, pulseoximetry determines hemoglobin saturation by measuring the changinginfrared optical properties of a finger. This signal may bedisambiguated and combined with pulmonary data to yield improvedinformation concerning lung function.

In one embodiment to monitor and predict stroke attack, a Doppler sensoris applied to detect fluids in the brain. The Doppler data is used todetect brain edema, which is defined as an increase in the water contentof cerebral tissue which then leads to an increase in overall brainmass. Two types of brain edema are vasogenic or cytotoxic. Vasogenicedema is a result of increased capillary permeability. Cytotoxic edemareflects the increase of brain water due to an osmotic imbalance betweenplasma and the brain extracellular fluid. Cerebral edema in brainswelling contributes to the increase in intracranial pressure and anearly detection leads to timely stroke intervention.

In yet another embodiment, a trans-cranial Doppler velocimetry sensorprovides a non-invasive technique for measuring blood flow in the brain.An RF beam from a transducer is directed through the skull to produce awaveform of blood flow in the arteries using Doppler techniques. Thedata collected to determine the blood flow may include values such asthe pulse cycle, blood flow velocity, end diastolic velocity, peaksystolic velocity, mean flow velocity, total volume of cerebral bloodflow, flow acceleration, the mean blood pressure in an artery, and thepulsatility index, or impedance to flow through a vessel. From thisdata, the condition of an artery may be derived, those conditionsincluding stenosis, vasoconstriction, irreversible stenosis,vasodilation, compensatory vasodilation, hyperemic vasodilation,vascular failure, compliance, breakthrough, and pseudo-normalization.

In addition to the above techniques to detect stroke attack, the systemcan detect numbness or weakness of the face, arm or leg, especially onone side of the body. The system detects sudden confusion, troublespeaking or understanding, sudden trouble seeing in one or both eyes,sudden trouble walking, dizziness, loss of balance or coordination, orsudden, severe headache with no known cause.

In one embodiment to detect heart attack, the system detects discomfortin the center of the chest that lasts more than a few minutes, or thatgoes away and comes back. Symptoms can include pain or discomfort in oneor both arms, the back, neck, jaw or stomach. The system can alsomonitor for shortness of breath which may occur with or without chestdiscomfort. Other signs may include breaking out in a cold sweat, nauseaor lightheadedness.

In order to best analyze a patient's risk of stroke, additional patientdata is utilized by a stroke risk analyzer. This data may includepersonal data, such as date of birth, ethnic group, sex, physicalactivity level, and address. The data may further include clinical datasuch as a visit identification, height, weight, date of visit, age,blood pressure, pulse rate, respiration rate, and so forth. The data mayfurther include data collected from blood work, such as the antinuclearantibody panel, B-vitamin deficiency, C-reactive protein value, calciumlevel, cholesterol levels, entidal CO2, fibromogin, amount of folicacid, glucose level, hematocrit percentage, H-pylori antibodies,hemocysteine level, hypercapnia, magnesium level, methyl maloric acidlevel, platelets count, potassium level, sedrate (ESR), serumosmolality, sodium level, zinc level, and so forth. The data may furtherinclude the health history data of the patient, including alcoholintake, autoimmune diseases, caffeine intake, carbohydrate intake,carotid artery disease, coronary disease, diabetes, drug abuse,fainting, glaucoma, head injury, hypertension, lupus, medications,smoking, stroke, family history of stroke, surgery history, for example.

In one embodiment, data driven analyzers may be used to track thepatient's risk of stroke or heart attack. These data driven analyzersmay incorporate a number of models such as parametric statisticalmodels, non-parametric statistical models, clustering models, nearestneighbor models, regression methods, and engineered (artificial) neuralnetworks. Prior to operation, data driven analyzers or models of thepatient stoke patterns are built using one or more training sessions.The data used to build the analyzer or model in these sessions aretypically referred to as training data. As data driven analyzers aredeveloped by examining only training examples, the selection of thetraining data can significantly affect the accuracy and the learningspeed of the data driven analyzer. One approach used heretoforegenerates a separate data set referred to as a test set for trainingpurposes. The test set is used to avoid overfitting the model oranalyzer to the training data. Overfitting refers to the situation wherethe analyzer has memorized the training data so well that it fails tofit or categorize unseen data. Typically, during the construction of theanalyzer or model, the analyzer's performance is tested against the testset. The selection of the analyzer or model parameters is performediteratively until the performance of the analyzer in classifying thetest set reaches an optimal point. At this point, the training processis completed. An alternative to using an independent training and testset is to use a methodology called cross-validation. Cross-validationcan be used to determine parameter values for a parametric analyzer ormodel for a non-parametric analyzer. In cross-validation, a singletraining data set is selected. Next, a number of different analyzers ormodels are built by presenting different parts of the training data astest sets to the analyzers in an iterative process. The parameter ormodel structure is then determined on the basis of the combinedperformance of all models or analyzers. Under the cross-validationapproach, the analyzer or model is typically retrained with data usingthe determined optimal model structure.

In general, multiple dimensions of a user's EEG, EKG, BI, ultrasound,optical, acoustic, electromagnetic, or electrical parameters are encodedas distinct dimensions in a database. A predictive model, including timeseries models such as those employing autoregression analysis and otherstandard time series methods, dynamic Bayesian networks and ContinuousTime Bayesian Networks, or temporal Bayesian-network representation andreasoning methodology, is built, and then the model, in conjunction witha specific query makes target inferences. Bayesian networks provide notonly a graphical, easily interpretable alternative language forexpressing background knowledge, but they also provide an inferencemechanism; that is, the probability of arbitrary events can becalculated from the model. Intuitively, given a Bayesian network, thetask of mining interesting unexpected patterns can be rephrased asdiscovering item sets in the data which are much more—or muchless—frequent than the background knowledge suggests. These cases areprovided to a learning and inference subsystem, which constructs aBayesian network that is tailored for a target prediction. The Bayesiannetwork is used to build a cumulative distribution over events ofinterest.

In another embodiment, a genetic algorithm (GA) search technique can beused to find approximate solutions to identifying the user's strokerisks or heart attack risks. Genetic algorithms are a particular classof evolutionary algorithms that use techniques inspired by evolutionarybiology such as inheritance, mutation, natural selection, andrecombination (or crossover). Genetic algorithms are typicallyimplemented as a computer simulation in which a population of abstractrepresentations (called chromosomes) of candidate solutions (calledindividuals) to an optimization problem evolves toward better solutions.Traditionally, solutions are represented in binary as strings of 0s and1s, but different encodings are also possible. The evolution starts froma population of completely random individuals and happens ingenerations. In each generation, the fitness of the whole population isevaluated, multiple individuals are stochastically selected from thecurrent population (based on their fitness), modified (mutated orrecombined) to form a new population, which becomes current in the nextiteration of the algorithm.

Substantially any type of learning system or process may be employed todetermine the stroke or heart attack patterns so that unusual events canbe flagged.

In one embodiment, clustering operations are performed to detectpatterns in the data. In another embodiment, a neural network is used torecognize each pattern as the neural network is quite robust atrecognizing user habits or patterns. Once the treatment features havebeen characterized, the neural network then compares the input userinformation with stored templates of treatment vocabulary known by theneural network recognizer, among others. The recognition models caninclude a Hidden Markov Model (HMM), a dynamic programming model, aneural network, a fuzzy logic, or a template matcher, among others.These models may be used singly or in combination.

Dynamic programming considers all possible points within the permitteddomain for each value of i. Because the best path from the current pointto the next point is independent of what happens beyond that point.Thus, the total cost of [i(k), j(k)] is the cost of the point itselfplus the cost of the minimum path to it. Preferably, the values of thepredecessors can be kept in an MxN array, and the accumulated cost keptin a 2xN array to contain the accumulated costs of the immediatelypreceding column and the current column. However, this method requiressignificant computing resources. For the recognizer to find the optimaltime alignment between a sequence of frames and a sequence of nodemodels, it must compare most frames against a plurality of node models.One method of reducing the amount of computation required for dynamicprogramming is to use pruning. Pruning terminates the dynamicprogramming of a given portion of user habit information against a giventreatment model if the partial probability score for that comparisondrops below a given threshold. This greatly reduces computation.

Considered to be a generalization of dynamic programming, a hiddenMarkov model is used in the preferred embodiment to evaluate theprobability of occurrence of a sequence of observations O(1), O(2), . .. O(t), . . . , O(T), where each observation O(t) may be either adiscrete symbol under the VQ approach or a continuous vector. Thesequence of observations may be modeled as a probabilistic function ofan underlying Markov chain having state transitions that are notdirectly observable. In one embodiment, the Markov network is used tomodel a number of user habits and activities. The transitions betweenstates are represented by a transition matrix A=[a(i,j)]. Each a(i,j)term of the transition matrix is the probability of making a transitionto state j given that the model is in state i. The output symbolprobability of the model is represented by a set of functions B=[b(j)(O(t)], where the b(j) (O(t) term of the output symbol matrix is theprobability of outputting observation O(t), given that the model is instate j. The first state is always constrained to be the initial statefor the first time frame of the utterance, as only a prescribed set ofleft to right state transitions are possible. A predetermined finalstate is defined from which transitions to other states cannot occur.Transitions are restricted to reentry of a state or entry to one of thenext two states. Such transitions are defined in the model as transitionprobabilities. Although the preferred embodiment restricts the flowgraphs to the present state or to the next two states, one skilled inthe art can build an HMM model without any transition restrictions,although the sum of all the probabilities of transitioning from anystate must still add up to one. In each state of the model, the currentfeature frame may be identified with one of a set of predefined outputsymbols or may be labeled probabilistically. In this case, the outputsymbol probability b(j) O(t) corresponds to the probability assigned bythe model that the feature frame symbol is O(t). The model arrangementis a matrix A=[a(i,j)] of transition probabilities and a technique ofcomputing B=b(j) O(t), the feature frame symbol probability in state j.The Markov model is formed for a reference pattern from a plurality ofsequences of training patterns and the output symbol probabilities aremultivariate Gaussian function probability densities. The patient habitinformation is processed by a feature extractor. During learning, theresulting feature vector series is processed by a parameter estimator,whose output is provided to the hidden Markov model. The hidden Markovmodel is used to derive a set of reference pattern templates, eachtemplate representative of an identified pattern in a vocabulary set ofreference treatment patterns. The Markov model reference templates arenext utilized to classify a sequence of observations into one of thereference patterns based on the probability of generating theobservations from each Markov model reference pattern template. Duringrecognition, the unknown pattern can then be identified as the referencepattern with the highest probability in the likelihood calculator. TheHMM template has a number of states, each having a discrete value.However, because treatment pattern features may have a dynamic patternin contrast to a single value. The addition of a neural network at thefront end of the HMM in an embodiment provides the capability ofrepresenting states with dynamic values. The input layer of the neuralnetwork comprises input neurons. The outputs of the input layer aredistributed to all neurons in the middle layer. Similarly, the outputsof the middle layer are distributed to all output states, which normallywould be the output layer of the neuron. However, each output hastransition probabilities to itself or to the next outputs, thus forminga modified HMM. Each state of the thus formed HMM is capable ofresponding to a particular dynamic signal, resulting in a more robustHMM. Alternatively, the neural network can be used alone withoutresorting to the transition probabilities of the HMM architecture.

The automated analyzer can also consider related pathologies inanalyzing a patient's risk of stroke, including but not limited togastritis, increased intracranial pressure, sleep disorders, smallvessel disease, and vasculitis.

In one embodiment, the processor and transceiver on the watch, thepatch(es) and the base station conform to the Zigbee protocol. ZigBee isa cost-effective, standards-based wireless networking solution thatsupports low data-rates, low-power consumption, security, andreliability. Single chip Zigbee controllers with wireless transceiversbuilt-in include the Chipcon/Ember CC2420: Single-chip 802.15.4 radiotransceiver and the FreeScale single chip Zigbee and microcontroller. Invarious embodiments, the processor communicates with a Z axisaccelerometer measures the patient's up and down motion and/or an X andY axis accelerometer measures the patient's forward and side movements.In one embodiment, EKG and/or blood pressure parameters can be capturedby the processor. The controllers upload the captured data when thememory is full or while in wireless contact with other Zigbee nodes.

The wristwatch device can also be used to control home automation. Theuser can have flexible management of lighting, heating and coolingsystems from anywhere in the home. The watch automates control ofmultiple home systems to improve conservation, convenience and safety.The watch can capture highly detailed electric, water and gas utilityusage data and embed intelligence to optimize consumption of naturalresources. The system is convenient in that it can be installed,upgraded and networked without wires. The patient can receive automaticnotification upon detection of unusual events in his or her home. Forexample, if smoke or carbon monoxide detectors detect a problem, thewrist-watch can buzz or vibrate to alert the user and the central hubtriggers selected lights to illuminate the safest exit route.

In another embodiment, the watch serves a key fob allowing the user towirelessly unlock doors controlled by Zigbee wireless receiver. In thisembodiment, when the user is within range, the door Zigbee transceiverreceives a request to unlock the door, and the Zigbee transceiver on thedoor transmits an authentication request using suitable securitymechanism. Upon entry, the Zigbee doorlock device sends access signalsto the lighting, air-conditioning and entertainment systems, amongothers. The lights and temperature are automatically set topre-programmed preferences when the user's presence is detected.

Although Zigbee is mentioned as an exemplary protocol, other protocolssuch as UWB, Bluetooth, WiFi and WiMAX can be used as well.

While the foregoing addresses the needs of the elderly, the system canassist infants as well. Much attention has been given to ways to reducea risk of dying from Sudden Infant Death Syndrome (SIDS), an afflictionwhich threatens infants who have died in their sleep for heretoforeunknown reasons. Many different explanations for this syndrome and waysto prevent the syndrome are found in the literature. It is thought thatinfants which sleep on their backs may be at risk of death because ofthe danger of formula regurgitation and liquid aspiration into thelungs. It has been thought that infants of six (6) months or less do nothave the motor skills or body muscular development to regulate movementsresponsive to correcting breathing problems that may occur during sleep.

In an exemplary system to detect and minimize SIDS problem in an infantpatient, a diaper pad is used to hold an array of integrated sensors andthe pad can be placed over a diaper, clothing, or blanket. Theintegrated sensors can provide data for measuring position, temperature,sound, vibration, movement, and optionally other physical propertiesthrough additional sensors. Each pad can have sensors that provide oneor more of the above data. The sensors can be added or removed asnecessary depending on the type of data being collected.

The sensor can be water proof and disposable. The sensor can be switchon/off locally or remotely. The sensor can be removable or clip oneasily. The sensor can store or beam out information for analysispurpose, e.g. store body temperature every 5 seconds. The sensor can beturn-on for other purposed, e.g. diaper wet, it will beep and allow ababy care provider to take care of the business in time. The array ofsensors can be self selective, e.g., when one sensor can detect strongheart beat, it will turn off others to do so.

The sensor can be used for drug delivery system, e.g. when patient hasabdomen pain, soothing drug can be applied, based on the level of painthe sensor detects, different dose of drugs will be applied.

The array of sensors may allow the selection and analysis of zones ofsensors in the areas of interest such as the abdomen area. Each sensorarray has a low spatial resolution: approximately 10 cm between eachsensor. In addition to lower cost due to the low number of sensors, itis also possible to modify the data collection rate from certain sensorsthat are providing high-quality data. Other sensors may include thoseworn on the body, such as in watch bands, finger rings, or adhesivesensors, but telemetry, not wires, would be used to communicate with thecontroller.

The sensor can be passive device such as a reader, which mounted nearthe crib can active it from time to time. In any emergency situation,the sensor automatically signals a different state which the reader candetect.

The sensor can be active and powered by body motion or body heat. Thesensor can detect low battery situation and warn the user to provide areplacement battery.

In one embodiment, a plurality of sensors attached to the infantcollects the vital parameters. For example, the sensors can be attachedto the infant's clothing (shirt or pant), diaper, undergarment or bedsheet, bed linen, or bed spread.

The patient may wear one or more sensors, for example devices forsensing EMG, EKG, blood pressure, sugar level, weight, temperature andpressure, among others. In one embodiment, an optical temperature sensorcan be used. In another embodiment, a temperature thermistor can be usedto sense patient temperature. In another embodiment, a fat scale sensorcan be used to detect the patient's fat content. In yet anotherembodiment, a pressure sensor such as a MEMS sensor can be used to sensepressure on the patient.

The CPU measures the time duration between the sequential pulses andconverts each such measurement into a corresponding timing measurementindicative of heart rate. The CPU also processes a predetermined numberof most recently occurring timing measurements in a prescribed fashion,to produce an estimate of heartbeat rate for display on a display deviceon the watch and/or for transmission over the wireless network. Thisestimate is updated with the occurrence of each successive pulse.

In one embodiment, the CPU produces the estimate of heartbeat rate byfirst averaging a plurality of measurements, then adjusting theparticular one of the measurements that differs most from the average tobe equal to that average, and finally computing an adjusted averagebased on the adjusted set of measurements. The process may repeat theforegoing operations a number of times so that the estimate of heartbeatrate is substantially unaffected by the occurrence of heartbeatartifacts.

The CPU measures the time durations between the successive pulses andestimates the heartbeat rate. The time durations between the successivepulses of the pulse sequence signal provides an estimate of heartbeatrate. Each time duration measurement is first converted to acorresponding rate, preferably expressed in beats per minute (bpm), andthen stored in a file, taking the place of the earliest measurementpreviously stored. After a new measurement is entered into the file, thestored measurements are averaged, to produce an average ratemeasurement. The CPU optionally determines which of the storedmeasurements differs most from the average, and replaces thatmeasurement with the average.

Upon initiation, the CPU increments a period timer used in measuring thetime duration between successive pulses. This timer is incremented insteps of about two milliseconds in one embodiment. It is then determinedwhether or not a pulse has occurred during the previous twomilliseconds. If it has not, the CPU returns to the initial step ofincrementing the period timer. If a heartbeat has occurred, on the otherhand, the CPU converts the time duration measurement currently stored inthe period timer to a corresponding heartbeat rate, preferably expressedin bpm. After the heartbeat rate measurement is computed, the CPUdetermines whether or not the computed rate is intermediate prescribedthresholds of 20 bpm and 240 bpm. If it is not, it is assumed that thedetected pulse was not in fact a heartbeat and the period timer iscleared.

Waveform averaging can be used to reduce noise. It reinforces thewaveform of interest by minimizing the effect of any random noise. Thesepulses were obtained when the arm was motionless. If the arm was movedwhile capturing the data the waveform did not look nearly as clean.That's because motion of the arm causes the sonic vibrations to enterthe piezo film through the arm or by way of the cable. An accelerometeris used to detect arm movement and used to remove inappropriate datacapture.

In another embodiment, the automatic identification of the first,second, third and fourth heart sounds (S1, S2, S3, S4) is done. In yetanother embodiment, based on the heart sound, the system analyzes thepatient for mitral valve prolapse. The system performs a time-frequencyanalysis of an acoustic signal emanating from the subject'scardiovascular system and examines the energy content of the signal inone or more frequency bands, particularly higher frequency bands, inorder to determine whether a subject suffers from mitral valve prolapse.

In one exemplary monitoring service providing system, such as anemergency service providing system, the system includes a communicationnetwork (e.g., the Public Switch Telephone Network or PSTN or POTS), awide area communication network (e.g., TCP/IP network) in call centers.The communication network receives calls destined for one of the callcenters. In this regard, each call destined for one of the call centersis preferably associated with a particular patient, a call identifier ora call identifier of a particular set of identifiers. A call identifierassociated with an incoming call may be an identifier dialed orotherwise input by the caller. For example, the call centers may belocations for receiving calls from a particular hospital or nursinghome.

To network may analyze the automatic number information (ANI) and/orautomatic location information (ALI) associated with the call. In thisregard, well known techniques exist for analyzing the ANI and ALI of anincoming call to identify the call as originating from a particularcalling device or a particular calling area. Such techniques may beemployed by the network to determine whether an incoming call originatedfrom a calling device within an area serviced by the call centers.Moreover, if an incoming call originated from such an area and if theincoming call is associated with the particular call identifier referredto above, then the network preferably routes the call to a designatedfacility.

When a call is routed to the facility, a central data manager, which maybe implemented in software, hardware, or a combination thereof,processes the call according to techniques that will be described inmore detail hereafter and routes the call, over the wide area network,to one of the call centers depending on the ANI and/or ALI associatedwith the call. In processing the call, the central data manager mayconvert the call from one communication protocol to anothercommunication protocol, such as voice over internet protocol (VoIP), forexample, in order to increase the performance and/or efficiency of thesystem. The central data manager may also gather information to help thecall centers in processing the call. There are various techniques thatmay be employed by the central data manager to enhance the performanceand/or efficiency of the system, and examples of such techniques will bedescribed in more detail hereafter.

Various benefits may be realized by utilizing a central facility tointercept or otherwise receive a call from the network and to then routethe call to one of the call centers via WAN. For example, servingmultiple call centers with a central data manager, may help to reducetotal equipment costs. In this regard, it is not generally necessary toduplicate the processing performed by the central data manager at eachof the call centers. Thus, equipment at each of the call centers may bereduced. As more call centers are added, the equipment savings enabledby implementing equipment at the central data manager instead of thecall centers generally increases. Furthermore, the system is notdependent on any telephone company's switch for controlling the mannerin which data is communicated to the call centers. In this regard, thecentral data manager may receive a call from the network and communicatethe call to the destination call centers via any desirable communicationtechnique, such as VoIP, for example. Data security is another possiblebenefit of the exemplary system 10 as the central data manager is ableto store the data for different network providers associated withnetwork on different partitions.

In one embodiment for professional users such as hospitals and nursinghomes, a Central Monitoring Station provides alarm and vital signoversight for a plurality of patients from a single computerworkstation.

In one embodiment, software for the professional monitoring systemprovides a login screen to enter user name and password, together withdatabase credentials. In Select Record function, the user can select aperson, based on either entered or pre-selected criteria. From herenavigate to their demographics, medical record, etc. The system can showa persons demographics, includes aliases, people involved in their care,friends and family, previous addresses, home and work locations,alternative numbers and custom fields. The system can show all dataelements of a person's medical record. These data elements are not ‘hardwired’, but may be configured in the data dictionary to suit particularuser requirements. It is possible to create views of the record thatfilter it to show (for instance) just the medications or diagnosis, etc.Any data element can be designated ‘plan able’ in the data dictionaryand then scheduled. A Summary Report can be done. Example of a reportdisplayed in simple format, selecting particular elements and dates. Asmany of these reports as required can be created, going across all datain the system based on some criteria, with a particular selection offields and sorting, grouping and totaling criteria. Reports can becreated that can format and analyze any data stored on the server. Thesystem supports OLE controls and can include graphs, bar codes, etc.These can be previewed on screen, printed out or exported in a widevariety of formats. The system also maintains a directory of allorganizations the administrator wishes to record as well as your own.These locations are then used to record the location for elements of themedical record (where applicable), work addresses for people involved inthe care and for residential addresses for people in residential care.The data elements that form the medical record are not ‘hard wired’ (iepredefined) but may be customized by the users to suit current andfuture requirements.

More details on the system shown in FIG. 3 are discussed in co-pendingU.S. application Ser. Nos. 11/768,381; 11/433,900 (May 12, 2006);10/938,783 (Sep. 10, 2004); 11/252,279 (Oct. 16, 2005); 11/433,282 (May12, 2006); 11/439,631 (May 24, 2006); 11/480,206 (Jun. 30, 2006);11/512,630 (Aug. 30, 2006); 11/480,231 (Jun. 30, 2006); and 11/588,197(Oct. 24, 2006), the contents of which are incorporated by reference.

FIG. 4A shows an exemplary process to continuously determine bloodpressure of a patient. The process generates a blood pressure model of apatient (2002); determines a heart rate and/or blood flow velocity usinga Doppler radar transducer (2004); and provides the heart rate and/orblood flow velocity to the blood pressure model to continuously estimateblood pressure (2006).

FIG. 4B shows another exemplary process to continuously determine bloodpressure of a patient. First, during an initialization mode, amonitoring device and calibration device are attached to patient (2010).The monitoring device generates patient heart rate and/or blood flowvelocity using Doppler radar, while actual blood pressure is measured bya calibration device (2012). Next, the process generates a bloodpressure model based on the heart rate or blood flow velocity and theactual blood pressure (2014). Once this is done, the calibration devicecan be removed (2016). Next, during an operation mode, the processperiodically samples heart rate or blood flow velocity from themonitoring device on a real-time basis (18) and provides the heart rateor blood flow velocity as input information to the blood pressure modelto estimate blood pressure (20). This process can be done incontinuously or periodically as specified by a user.

In one embodiment, to determine blood flow velocity, RF pulses aregenerated and transmitted into the artery. These pulses are reflected byvarious structures or entities within the artery (such as the arterywalls, and the red blood cells within the subject's blood), andsubsequently received as frequency shifts by the RF transducer. Next,the blood flow velocity is determined. In this process, the frequenciesof those echoes reflected by blood cells within the blood flowing in theartery differ from that of the transmitted acoustic pulses due to themotion of the blood cells. This well known “Doppler shift” in frequencyis used to calculate the blood flow velocity. In one embodiment fordetermining blood flow velocity, the Doppler frequency is used todetermine mean blood velocity. For example, U.S. Pat. No. 6,514,211, thecontent of which is incorporated by reference, discusses blood flowvelocity using a time-frequency representation.

In one implementation, the system can obtain one or more numericalcalibration curves describing the patient's vital signs such as bloodpressure. The system can then direct energy such as RF energy (oralternatively infrared or ultrasound energy) at the patient's artery anddetecting reflections thereof to determine blood flow velocity from thedetected reflections. The system can numerically fit or map the bloodflow velocity to one or more calibration parameters describing avital-sign value. The calibration parameters can then be compared withone or more numerical calibration curves to determine the bloodpressure.

Additionally, the system can analyze blood pressure, and heart rate, andpulse oximetry values to characterize the user's cardiac condition.These programs, for example, may provide a report that featuresstatistical analysis of these data to determine averages, data displayedin a graphical format, trends, and comparisons to doctor-recommendedvalues.

In one embodiment, feed forward artificial neural networks (NNs) areused to classify valve-related heart disorders. The heart sounds arecaptured using the microphone or piezoelectric transducer. Relevantfeatures were extracted using several signal processing tools, discretewavelet transfer, fast fourier transform, and linear prediction coding.The heart beat sounds are processed to extract the necessary featuresby: a) denoising using wavelet analysis, b) separating one beat out ofeach record c) identifying each of the first heart sound (FHS) and thesecond heart sound (SHS). Valve problems are classified according to thetime separation between the FHS and the SHS relative to cardiac cycletime, namely whether it is greater or smaller than 20% of cardiac cycletime. In one embodiment, the NN comprises 6 nodes at both ends, with onehidden layer containing 10 nodes. In another embodiment, linearpredictive code (LPC) coefficients for each event were fed to twoseparate neural networks containing hidden neurons.

In another embodiment, a normalized energy spectrum of the sound data isobtained by applying a Fast Fourier Transform. The various spectralresolutions and frequency ranges were used as inputs into the NN tooptimize these parameters to obtain the most favorable results.

In another embodiment, the heart beats are denoised using six-stagewavelet decomposition, thresholding, and then reconstruction. Threefeature extraction techniques were used: the Decimation method, and thewavelet method. Classification of the heart diseases is done usingHidden Markov Models (HMMs).

In yet another embodiment, a wavelet transform is applied to a window oftwo periods of heart sounds. Two analyses are realized for the signalsin the window: segmentation of first and second heart sounds, and theextraction of the features. After segmentation, feature vectors areformed by using the wavelet detail coefficients at the sixthdecomposition level. The best feature elements are analyzed by usingdynamic programming.

In another embodiment, the wavelet decomposition and reconstructionmethod extract features from the heart sound recordings. An artificialneural network classification method classifies the heart sound signalsinto physiological and pathological murmurs. The heart sounds aresegmented into four parts: the first heart sound, the systolic period,the second heart sound, and the diastolic period. The following featurescan be extracted and used in the classification algorithm: a) Peakintensity, peak timing, and the duration of the first heart sound b) theduration of the second heart sound c) peak intensity of the aorticcomponent of S2(A2) and the pulmonic component of S2 (P2), the splittinginterval and the reverse flag of A2 and P2, and the timing of A2 d) theduration, the three largest frequency components of the systolic signaland the shape of the envelope of systolic murmur e) the duration thethree largest frequency components of the diastolic signal and the shapeof the envelope of the diastolic murmur.

In one embodiment, the time intervals between the ECG R-waves aredetected using an envelope detection process. The intervals between Rand T waves are also determined. The Fourier transform is applied to thesound to detect S1 and S2. To expedite processing, the system appliesFourier transform to detect S1 in the interval 0.1-0.5 R-R. The systemlooks for S2 the intervals R-T and 0.6 R-R. S2 has an aortic componentA2 and a pulmonary component P2. The interval between these twocomponents and its changes with respiration has clinical significance.A2 sound occurs before P2, and the intensity of each component dependson the closing pressure and hence A2 is louder than P2. The third heardsound S3 results from the sudden halt in the movement of the ventriclein response to filling in early diastole after the AV valves and isnormally observed in children and young adults. The fourth heart soundS4 is caused by the sudden halt of the ventricle in response to fillingin presystole due to atrial contraction.

In yet another embodiment, the S2 is identified and a normalizedsplitting interval between A2 and P2 is determined. If there is nooverlap, A2 and P2 are determined from the heart sound. When overlapexists between A2 and P2, the sound is dechirped for identification andextraction of A2 and P2 from S2. The A2-P2 splitting interval (SI) iscalculated by computing the cross-correlation function between A2 and P2and measuring the time of occurrence of its maximum amplitude. SI isthen normalized (NSI) for heart rate as follows: NSI=SI/cardiac cycletime. The duration of the cardiac cycle can be the average interval ofQRS waves of the ECG. It could also be estimated by computing the meaninterval between a series of consecutive S1 and S2 from the heart sounddata. A non linear regressive analysis maps the relationship between thenormalized NSI and PAP. A mapping process such as a curve-fittingprocedure determines the curve that provides the best fit with thepatient data. Once the mathematical relationship is determined, NSI canbe used to provide an accurate quantitative estimate of the systolic andmean PAP relatively independent of heart rate and systemic arterialpressure.

In another embodiment, the first heart sound (S1) is detected using atime-delayed neural network (TDNN). The network consists of a singlehidden layer, with time-delayed links connecting the hidden units to thetime-frequency energy coefficients of a Morlet wavelet decomposition ofthe input phonocardiogram (PCG) signal. The neural network operates on a200 msec sliding window with each time-delay hidden unit spanning 100msec of wavelet data.

In yet another embodiment, a local signal analysis is used with aclassifier to detect, characterize, and interpret sounds correspondingto symptoms important for cardiac diagnosis. The system detects aplurality of different heart conditions. Heart sounds are automaticallysegmented into a segment of a single heart beat cycle. Each segment arethen transformed using 7 level wavelet decomposition, based on Coifman4th order wavelet kernel. The resulting vectors 4096 values, are reducedto 256 element feature vectors, this simplified the neural network andreduced noise.

In another embodiment, feature vectors are formed by using the waveletdetail and approximation coefficients at the second and sixthdecomposition levels. The classification (decision making) is performedin 4 steps: segmentation of the first and second heart sounds,normalization process, feature extraction, and classification by theartificial neural network.

In another embodiment using decision trees, the system distinguishes (1)the Aortic Stenosis (AS) from the Mitral Regurgitation (MR) and (2) theOpening Snap (OS), the Second Heart Sound Split (A2_P2) and the ThirdHeart Sound (S3). The heart sound signals are processed to detect thefirst and second heart sounds in the following steps: a) waveletdecomposition, b) calculation of normalized average Shannon Energy, c) amorphological transform action that amplifies the sharp peaks andattenuates the broad ones d) a method that selects and recovers thepeaks corresponding to S1 and S2 and rejects others e) algorithm thatdetermines the boundaries of S1 and S2 in each heart cycle f) a methodthat distinguishes S1 from S2.

In one embodiment, heart sound is captured using a sound transducerlocated near the heart or near the carotid artery. Once the heart soundsignal has been digitized and captured into the memory, the digitizedheart sound signal is parameterized into acoustic features by a featureextractor. The output of the feature extractor is delivered to a soundrecognizer. The feature extractor can include the short time energy, thezero crossing rates, the level crossing rates, the filter-bank spectrum,the linear predictive coding (LPC), and the fractal method of analysis.In addition, vector quantization may be utilized in combination with anyrepresentation techniques. Further, one skilled in the art may use anauditory signal-processing model in place of the spectral models toenhance the system's robustness to noise and reverberation.

In one embodiment of the feature extractor, the digitized heart soundsignal series s(n) is put through a low-order filter, typically afirst-order finite impulse response filter, to spectrally flatten thesignal and to make the signal less susceptible to finite precisioneffects encountered later in the signal processing. The signal ispre-emphasized preferably using a fixed pre-emphasis network, orpreemphasizer. The signal can also be passed through a slowly adaptivepre-emphasizer. The preemphasized heart sound signal is next presentedto a frame blocker to be blocked into frames of N samples with adjacentframes being separated by M samples. In one implementation, frame 1contains the first 400 samples. The frame 2 also contains 400 samples,but begins at the 300th sample and continues until the 700th sample.Because the adjacent frames overlap, the resulting LPC spectral analysiswill be correlated from frame to frame. Each frame is windowed tominimize signal discontinuities at the beginning and end of each frame.The windower tapers the signal to zero at the beginning and end of eachframe. Preferably, the window used for the autocorrelation method of LPCis the Hamming window. A noise canceller operates in conjunction withthe autocorrelator to minimize noise. Noise in the heart sound patternis estimated during quiet periods, and the temporally stationary noisesources are damped by means of spectral subtraction, where theautocorrelation of a clean heart sound signal is obtained by subtractingthe autocorrelation of noise from that of corrupted heart sound. In thenoise cancellation unit, if the energy of the current frame exceeds areference threshold level, the heart is generating sound and theautocorrelation of coefficients representing noise is not updated.However, if the energy of the current frame is below the referencethreshold level, the effect of noise on the correlation coefficients issubtracted off in the spectral domain. The result is half-wave rectifiedwith proper threshold setting and then converted to the desiredautocorrelation coefficients. The output of the autocorrelator and thenoise canceller are presented to one or more parameterization units,including an LPC parameter unit, an FFT parameter unit, an auditorymodel parameter unit, a fractal parameter unit, or a wavelet parameterunit, among others. The LPC parameter is then converted into cepstralcoefficients. The cepstral coefficients are the coefficients of theFourier transform representation of the log magnitude spectrum. A filterbank spectral analysis, which uses the short-time Fourier transformation(STFT) may also be used alone or in conjunction with other parameterblocks. FFT is well known in the art of digital signal processing. Sucha transform converts a time domain signal, measured as amplitude overtime, into a frequency domain spectrum, which expresses the frequencycontent of the time domain signal as a number of different frequencybands. The FFT thus produces a vector of values corresponding to theenergy amplitude in each of the frequency bands. The FFT converts theenergy amplitude values into a logarithmic value which reducessubsequent computation since the logarithmic values are more simple toperform calculations on than the longer linear energy amplitude valuesproduced by the FFT, while representing the same dynamic range. Ways forimproving logarithmic conversions are well known in the art, one of thesimplest being use of a look-up table. In addition, the FFT modifies itsoutput to simplify computations based on the amplitude of a given frame.This modification is made by deriving an average value of the logarithmsof the amplitudes for all bands. This average value is then subtractedfrom each of a predetermined group of logarithms, representative of apredetermined group of frequencies. The predetermined group consists ofthe logarithmic values, representing each of the frequency bands. Thus,utterances are converted from acoustic data to a sequence of vectors ofk dimensions, each sequence of vectors identified as an acoustic frame,each frame represents a portion of the utterance. Alternatively,auditory modeling parameter unit can be used alone or in conjunctionwith others to improve the parameterization of heart sound signals innoisy and reverberant environments. In this approach, the filteringsection may be represented by a plurality of filters equally spaced on alog-frequency scale from 0 Hz to about 3000 Hz and having a prescribedresponse corresponding to the cochlea. The nerve fiber firing mechanismis simulated by a multilevel crossing detector at the output of eachcochlear filter. The ensemble of the multilevel crossing intervalscorresponds to the firing activity at the auditory nerve fiber-array.The interval between each successive pair of same direction, eitherpositive or negative going, crossings of each predetermined soundintensity level is determined and a count of the inverse of theseinterspike intervals of the multilevel detectors for each spectralportion is stored as a function of frequency. The resulting histogram ofthe ensemble of inverse interspike intervals forms a spectral patternthat is representative of the spectral distribution of the auditoryneural response to the input sound and is relatively insensitive tonoise The use of a plurality of logarithmically related sound intensitylevels accounts for the intensity of the input signal in a particularfrequency range. Thus, a signal of a particular frequency having highintensity peaks results in a much larger count for that frequency than alow intensity signal of the same frequency. The multiple levelhistograms of the type described herein readily indicate the intensitylevels of the nerve firing spectral distribution and cancel noiseeffects in the individual intensity level histograms. Alternatively, thefractal parameter block can further be used alone or in conjunction withothers to represent spectral information. Fractals have the property ofself similarity as the spatial scale is changed over many orders ofmagnitude. A fractal function includes both the basic form inherent in ashape and the statistical or random properties of the replacement ofthat shape in space. As is known in the art, a fractal generator employsmathematical operations known as local affine transformations. Thesetransformations are employed in the process of encoding digital datarepresenting spectral data. The encoded output constitutes a “fractaltransform” of the spectral data and consists of coefficients of theaffine transformations. Different fractal transforms correspond todifferent images or sounds.

Alternatively, a wavelet parameterization block can be used alone or inconjunction with others to generate the parameters. Like the FFT, thediscrete wavelet transform (DWT) can be viewed as a rotation in functionspace, from the input space, or time domain, to a different domain. TheDWT consists of applying a wavelet coefficient matrix hierarchically,first to the full data vector of length N, then to a smooth vector oflength N/2, then to the smooth-smooth vector of length N/4, and so on.Most of the usefulness of wavelets rests on the fact that wavelettransforms can usefully be severely truncated, or turned into sparseexpansions. In the DWT parameterization block, the wavelet transform ofthe heart sound signal is performed. The wavelet coefficients areallocated in a non-uniform, optimized manner. In general, large waveletcoefficients are quantized accurately, while small coefficients arequantized coarsely or even truncated completely to achieve theparameterization. Due to the sensitivity of the low-order cepstralcoefficients to the overall spectral slope and the sensitivity of thehigh-order cepstral coefficients to noise variations, the parametersgenerated may be weighted by a parameter weighing block, which is atapered window, so as to minimize these sensitivities. Next, a temporalderivator measures the dynamic changes in the spectra. Power featuresare also generated to enable the system to distinguish heart sound fromsilence.

After the feature extraction has been performed, the heart soundparameters are next assembled into a multidimensional vector and a largecollection of such feature signal vectors can be used to generate a muchsmaller set of vector quantized (VQ) feature signals by a vectorquantizer that cover the range of the larger collection. In addition toreducing the storage space, the VQ representation simplifies thecomputation for determining the similarity of spectral analysis vectorsand reduces the similarity computation to a look-up table ofsimilarities between pairs of codebook vectors. To reduce thequantization error and to increase the dynamic range and the precisionof the vector quantizer, the preferred embodiment partitions the featureparameters into separate codebooks, preferably three. In the preferredembodiment, the first, second and third codebooks correspond to thecepstral coefficients, the differenced cepstral coefficients, and thedifferenced power coefficients.

With conventional vector quantization, an input vector is represented bythe codeword closest to the input vector in terms of distortion. Inconventional set theory, an object either belongs to or does not belongto a set. This is in contrast to fuzzy sets where the membership of anobject to a set is not so clearly defined so that the object can be apart member of a set. Data are assigned to fuzzy sets based upon thedegree of membership therein, which ranges from 0 (no membership) to 1.0(full membership). A fuzzy set theory uses membership functions todetermine the fuzzy set or sets to which a particular data value belongsand its degree of membership therein.

To handle the variance of heart sound patterns of individuals over timeand to perform speaker adaptation in an automatic, self-organizingmanner, an adaptive clustering technique called hierarchical spectralclustering is used. Such speaker changes can result from temporary orpermanent changes in vocal tract characteristics or from environmentaleffects. Thus, the codebook performance is improved by collecting heartsound patterns over a long period of time to account for naturalvariations in speaker behavior. In one embodiment, data from the vectorquantizer is presented to one or more recognition models, including anHMM model, a dynamic time warping model, a neural network, a fuzzylogic, or a template matcher, among others. These models may be usedsingly or in combination.

In dynamic processing, at the time of recognition, dynamic programmingslides, or expands and contracts, an operating region, or window,relative to the frames of heart sound so as to align those frames withthe node models of each S1-S4 pattern to find a relatively optimal timealignment between those frames and those nodes. The dynamic processingin effect calculates the probability that a given sequence of framesmatches a given word model as a function of how well each such framematches the node model with which it has been time-aligned. The wordmodel which has the highest probability score is selected ascorresponding to the heart sound.

Dynamic programming obtains a relatively optimal time alignment betweenthe heart sound to be recognized and the nodes of each word model, whichcompensates for the unavoidable differences in speaking rates whichoccur in different utterances of the same word. In addition, sincedynamic programming scores words as a function of the fit between wordmodels and the heart sound over many frames, it usually gives thecorrect word the best score, even if the word has been slightlymisspoken or obscured by background sound. This is important, becausehumans often mispronounce words either by deleting or mispronouncingproper sounds, or by inserting sounds which do not belong.

In dynamic time warping (DTW), the input heart sound A, defined as thesampled time values A=a(1) . . . a(n), and the vocabulary candidate B,defined as the sampled time values B=b(1) . . . b(n), are matched up tominimize the discrepancy in each matched pair of samples. Computing thewarping function can be viewed as the process of finding the minimumcost path from the beginning to the end of the words, where the cost isa function of the discrepancy between the corresponding points of thetwo words to be compared. Dynamic programming considers all possiblepoints within the permitted domain for each value of i. Because the bestpath from the current point to the next point is independent of whathappens beyond that point. Thus, the total cost of [i(k), j(k)] is thecost of the point itself plus the cost of the minimum path to it.Preferably, the values of the predecessors can be kept in an MxN array,and the accumulated cost kept in a 2xN array to contain the accumulatedcosts of the immediately preceding column and the current column.However, this method requires significant computing resources. For theheart sound recognizer to find the optimal time alignment between asequence of frames and a sequence of node models, it must compare mostframes against a plurality of node models. One method of reducing theamount of computation required for dynamic programming is to usepruning. Pruning terminates the dynamic programming of a given portionof heart sound against a given word model if the partial probabilityscore for that comparison drops below a given threshold. This greatlyreduces computation, since the dynamic programming of a given portion ofheart sound against most words produces poor dynamic programming scoresrather quickly, enabling most words to be pruned after only a smallpercent of their comparison has been performed. To reduce thecomputations involved, one embodiment limits the search to that within alegal path of the warping.

A Hidden Markov model can be used in one embodiment to evaluate theprobability of occurrence of a sequence of observations O(1), O(2), . .. O(t), . . . , O(T), where each observation O(t) may be either adiscrete symbol under the VQ approach or a continuous vector. Thesequence of observations may be modeled as a probabilistic function ofan underlying Markov chain having state transitions that are notdirectly observable. The transitions between states are represented by atransition matrix A=[a(i,j)]. Each a(i,j) term of the transition matrixis the probability of making a transition to state j given that themodel is in state i. The output symbol probability of the model isrepresented by a set of functions B=[b(j)(O(t)], where the b(j)(O(t)term of the output symbol matrix is the probability of outputtingobservation O(t), given that the model is in state j. The first state isalways constrained to be the initial state for the first time frame ofthe utterance, as only a prescribed set of left-to-right statetransitions are possible. A predetermined final state is defined fromwhich transitions to other states cannot occur. Transitions arerestricted to reentry of a state or entry to one of the next two states.Such transitions are defined in the model as transition probabilities.For example, a heart sound pattern currently having a frame of featuresignals in state 2 has a probability of reentering state 2 of a (2,2), aprobability a (2,3) of entering state 3 and a probability of a (2,4)=1−a(2, 1)-a (2,2) of entering state 4. The probability a (2, 1) of enteringstate 1 or the probability a (2,5) of entering state 5 is zero and thesum of the probabilities a (2,1) through a (2,5) is one. Although thepreferred embodiment restricts the flow graphs to the present state orto the next two states, one skilled in the art can build an HMM modelwithout any transition restrictions.

The Markov model is formed for a reference pattern from a plurality ofsequences of training patterns and the output symbol probabilities aremultivariate Gaussian function probability densities. The heart soundtraverses through the feature extractor. During learning, the resultingfeature vector series is processed by a parameter estimator, whoseoutput is provided to the hidden Markov model. The hidden Markov modelis used to derive a set of reference pattern templates, each templaterepresentative of an identified S1-S4 pattern in a vocabulary set ofreference patterns. The Markov model reference templates are nextutilized to classify a sequence of observations into one of thereference patterns based on the probability of generating theobservations from each Markov model reference pattern template. Duringrecognition, the unknown pattern can then be identified as the referencepattern with the highest probability in the likelihood calculator.

In one embodiment, a heart sound analyzer detects Normal S1, Split S1,Normal S2, Normal split S2, Wide split S2, Paradoxical split S2, Fixedsplit S2, S3 right ventricle origin, S3 left ventricle origin, openingsnap, S4 right ventricle origin, S4 left ventricle origin, aorticejection sound, and pulmonic ejection sound, among others. The soundanalyzer can be an HMM type analyzer, a neural network type analyzer, afuzzy logic type analyzer, a genetic algorithm type analyzer, arule-based analyzer, or any suitable classifier. The heart sound data iscaptured, filtered, and the major features of the heart sound aredetermined and then operated by a classifier such as HMM or neuralnetwork, among others.

The analyzer can detect S1, whose major audible components are relatedto mitral and tricuspid valve closure. Mitral (MI) closure is the firstaudible component of the first sound. It normally occurs beforetricuspid (T1) closure, and is of slightly higher intensity than T1. Asplit of the first sound occurs when both components that make up thesound are separately distinguishable. In a normally split first sound,the mitral and tricuspid components are 20 to 30 milliseconds apart.Under certain conditions a wide or abnormally split first sound can beheard. An abnormally wide split first sound can be due to eitherelectrical or mechanical causes, which create asynchrony of the twoventricles. Some of the electrical causes may be right bundle branchblock, premature ventricular beats and ventricular tachycardia. Anapparently wide split can be caused by another sound around the time ofthe first. The closure of the aortic and pulmonic valves contributes tosecond sound production. In the normal sequence, the aortic valve closesbefore the pulmonic valve. The left sided mechanical events normallyprecede right sided events.

The system can analyze the second sound S2. The aortic (A2) component ofthe second sound is the loudest of the two components and is discernibleat all auscultation sites, but especially well at the base. The pulmonic(P2) component of the second sound is the softer of the two componentsand is usually audible at base left. A physiological split occurs whenboth components of the second sound are separately distinguishable.Normally this split sound is heard on inspiration and becomes single onexpiration. The A2 and P2 components of the physiological split usuallycoincide, or are less than 30 milliseconds apart during expiration andoften moved to around 50 to 60 milliseconds apart by the end ofinspiration. The physiological split is heard during inspiration becauseit is during that respiratory cycle that intrathoracic pressure drops.This drop permits more blood to return to the right heart. The increasedblood volume in the right ventricle results in a delayed pulmonic valveclosure. At the same time, the capacity of the pulmonary vessels in thelung is increased, which results in a slight decrease in the bloodvolume returning to the left heart. With less blood in the leftventricle, its ejection takes less time, resulting in earlier closing ofthe aortic valve. Therefore, the net effect of inspiration is to causeaortic closure to occur earlier, and pulmonary closure to occur later.Thus, a split second is heard during inspiration, and a single secondsound is heard during expiration. A reversed (paradoxical) split of thesecond sound occurs when there is a reversal of the normal closuresequence with pulmonic closure occurring before aortic. Duringinspiration the second sound is single, and during expiration the secondsound splits. This paradoxical splitting of the second sound may beheard when aortic closure is delayed, as in marked volume or pressureloads on the left ventricle (i.e., aortic stenosis) or with conductiondefects which delay left ventricular depolarization (i.e., left bundlebranch block). The normal physiological split second sound can beaccentuated by conditions that cause an abnormal delay in pulmonicvalve-1 closure. Such a delay may be due to an increased volume in theright ventricle as o compared with the left (atrial septal defect, orventricular septal defect); chronic right ventricular outflowobstruction (pulmonic stenosis); acute or chronic dilatation of the.right ventricle due to sudden rise in pulmonary artery pressure(pulmonary embolism); electrical delay or activation of AA the rightventricle (right bundle branch block); decreased elastic recoil of thepulmonary artery (idiopathic dilatation of the pulmonary artery). Thewide split has a duration of 40 to 50 'milliseconds, compared to thenormal physiologic split of 30 milliseconds. Fixed splitting of thesecond sound refers to split sound which displays little or norespiratory variation. The two components making up the sound occur intheir normal sequence, but the ventricles are unable to change theirvolumes with respiration. This finding is typical in atrial septaldefect, but is occasionally heard in congestive heart failure. The fixedsplit is heard best at base left with the diaphragm.

The third heart sound is also of low frequency, but it is heard justafter the second heart sound. It occurs in early diastole, during thetime of rapid ventricular filling. This sound occurs about 140 to 160milliseconds after the second sound. The S3 is often heard in normalchildren or young adults but when heard in individuals over the age of40 it usually reflects cardiac disease characterized by ventriculardilatation, decreased systolic function, and elevated ventriculardiastolic filling pressure. The nomenclature includes the termventricular gallop, protodiastolic gallop, S3 gallop, or the morecommon, S3. When normal it is referred to as a physiological third heartsound, and is usually not heard past the age of forty. The abnormal, orpathological third heart sound, may be heard in individuals withcoronary artery disease, cardiomyopathies, incompetent valves, left toright shunts, Ventricular Septal Defect (VSD), or Patent DuctusArteriosus (PDA). The pathological S3 may be the first clinical sign ofcongestive heart failure. The fourth heart sound is a low frequencysound heard just before the first heart sound, usually preceding thissound by a longer interval than that separating the two components ofthe normal first sound. It has also been known as an “atrial gallop”, a“presystolic gallop”, and an “S4 gallop”. It is most commonly known asan “S4”.

The S4 is a diastolic sound, which occurs during the late diastolicfilling phase at the time when the atria contract. When the ventricleshave a decreased compliance, or are receiving an increased diastolicvolume, they generate a low frequency vibration, the S4. Someauthorities believe the S4 may be normal in youth, but is seldomconsidered normal after the age of 20. The abnormal or pathological S4is heard in primary myocardial disease, coronary artery disease,hypertension, and aortic and pulmonic stenosis. The S4 may have itsorigin in either the left or right heart. The S4 of left ventricularorigin is best heard at the apex, with the patient supine, or in theleft lateral recumbent position. Its causes include severe hypertension,aortic stenosis, cardiomyopathies, and left ventricular myocardialinfarctions. In association with ischemic heart disease the S4 is oftenloudest during episodes of angina pectoris or may occur early after anacute myocardial infarction, often becoming fainter as the patientimproves. The S4 of right ventricular origin is best heard at the leftlateral sternal border. It is usually accentuated with inspiration, andmay be due to pulmonary stenosis, pulmonary hypertension, or rightventricular myocardial infarction. When both the third heart sound and afourth heart sound are present, with a normal heart rate, 60-100 heartbeats per minute, the four sound cadence of a quadruple rhythm may beheard.

Ejection sounds are high frequency clicky sounds occurring shortly afterthe first sound with the onset of ventricular ejection. They areproduced by the opening of the semilunar valves, aortic or pulmonic,either when one of these valves is diseased, or when ejection is rapidthrough a normal valve. They are heard best at the base, and may be ofeither aortic or pulmonic origin. Ejection sounds of aortic origin oftenradiate widely and may be heard anywhere on a straight line from thebase right to the apex. Aortic ejection sounds are most typically heardin patients with valvular aortic stenosis, but are occasionally heard invarious other conditions, such as aortic insufficiency, coarctation ofthe aorta, or aneurysm of the ascending aorta. Ejection sounds ofpulmonic origin are heard anywhere on a straight line from base left,where they are usually best heard, to the epigastrium. Pulmonic ejectionsounds are typically heard in pulmonic stenosis, but may be encounteredin pulmonary hypertension, atrial septal defects (ASD) or in conditionscausing enlargement of the pulmonary artery. Clicks are high frequencysounds which occur in systole, either mid, early, or late. The clickgenerally occurs at least 100 milliseconds after the first sound. Themost common cause of the click is mitral valve prolapse. The clicks ofmitral origin are best heard at the apex, or toward the left lateralsternal border. The click will move closer to the first sound whenvolume to the ventricle is reduced, as occurs in standing or theValsalva maneuver. The opening snap is a short high frequency sound,which occurs after the second heart sound in early diastole. It usuallyfollows the second sound by about 60 to 100 milliseconds. It is mostfrequently the result of the sudden arrest of the opening of the mitralvalve, occurring in mitral stenosis, but may also be encountered inconditions producing increased flow through this valve (i.e., VSD orPDA). In tricuspid stenosis or in association with increased flow acrossthe tricuspid valve, as in ASD, a tricuspid opening snap may be heard.The tricuspid opening snap is loudest at the left lateral sternalborder, and becomes louder with inspiration.

Murmurs are sustained noises that are audible during the time periods ofsystole, diastole, or both. They are basically produced by thesefactors: 1) Backward regurgitation through a leaking valve or septaldefect; 2) Forward flow through a narrowed or deformed valve or conduitor through an arterial venous connection; 3) High rate of blood flowthrough a normal or abnormal valve; 4) Vibration of loose structureswithin the heart (i.e., chordae tendineae or valvular tissue). Murmursthat occur when the ventricles are contracting, that is, during systole,are referred to as systolic murmurs. Murmurs occurring when theventricles are relaxed and filling, that is during diastole, arereferred to as diastolic murmurs. There are six characteristics usefulin murmur identification and differentiation:

-   -   1) Location or the valve area over which the murmur is best        heard. This is one clue to the origin of the murmur. Murmurs of        mitral origin are usually best heard at the apex. Tricuspid        murmurs at the lower left lateral sternal border, and pulmonic        murmurs at base left. Aortic systolic murmurs are best heard at        base right, and aortic diastolic murmurs at Erb's point, the        third intercostal space to the left of the sternum.    -   2) Frequency (pitch). Low, medium, or high.    -   3) Intensity.    -   4) Quality.    -   5) Timing. (Occurring during systole, diastole, or both).    -   6) Areas where the sound is audible in addition to the area over        which it is heard best.

Systolic murmurs are sustained noises that are audible during the timeperiod of systole, or the period between S1 and S2. Forward flow acrossthe aortic or pulmonic valves, or regurgitant flow from the mitral ortricuspid valve may produce a systolic murmur. Systolic murmurs may benormal, and can represent normal blood flow, i.e., thin chest, babiesand children, or increased blood flow, i.e., pregnant women. Earlysystolic murmurs begin with or shortly after the first sound and peak inthe first third of systole. Early murmurs have the greatest intensity inthe early part of the cycle. The commonest cause is the innocent murmurof childhood (to be discussed later). A small ventricular septal defect(VSD) occasionally causes an early systolic murmur. The early systolicmurmur of a small VSD begins with S1 and stops in mid systole, becauseas ejection continues and the ventricular size decreases, the smalldefect is sealed shut, causing the murmur to soften or cease. Thismurmur is characteristic of the type of children's VSD located in themuscular portion of the ventricular septum. This defect may disappearwith age. A mid-systolic murmur begins shortly after the first sound,peaks in the middle of systole, and does not quite extend to the secondsound. It is the crescendo decrescendo murmur which builds up anddecrease symmetrically. It is also known as an ejection murmur. It mostcommonly is due to forward blood flow through a normal, narrow orirregular valve, i.e., aortic or pulmonic stenosis. The murmur beginswhen the pressure in the respective ventricle exceeds the aortic orpulmonary arterial pressure. The most characteristic feature of thismurmur is its cessation before the second sound, thus leaving thislatter sound identifiable as a discrete entity. This type of murmur iscommonly heard in normal individuals, particularly in the young, whousually have increased blood volumes flowing over normal valves. In thissetting the murmur is usually short, with its peak intensity early insystole, and is soft, seldom over 2 over 6 in intensity. It is thendesignated as an innocent murmur. In order for a murmur to be classifiedas innocent (i.e. normal), the following are present:

-   -   1) Normal splitting of the second sound together with absence of        abnormal sounds or murmurs, such as ejection sounds, diastolic        murmurs, etc.    -   2) Normal jugular venus and carotid pulses    -   3) Normal precordial pulsations or palpation, and    -   4) Normal chest x-ray and ECG

Obstruction or stenosis across the aortic or pulmonic valves also maygive rise to a murmur of this type. These murmurs are usually longer andlouder than the innocent murmur, and reach a peak intensity inmid-systole. The murmur of aortic stenosis is harsh in quality and isheard equally well with either the bell or the diaphragm. It is heardbest at base right, and radiates to the apex and to the neckbilaterally.

An early diastolic murmur begins with a second sound, and peaks in thefirst third of diastole. Common causes are aortic regurgitation andpulmonic regurgitation. The early diastolic murmur of aorticregurgitation usually has a high frequency blowing quality, is heardbest with a diaphragm at Erb's point, and radiates downward along theleft sternal border. Aortic regurgitation tends to be of short duration,and heard best on inspiration. This respiratory variation is helpful indifferentiating pulmonic regurgitation from aortic regurgitation. Amid-diastolic murmur begins after the second sound and peaks inmid-diastole. Common causes are mitral stenosis, and tricuspid stenosis.The murmur of mitral stenosis is a low frequency, crescendo de crescendorumble, heard at the apex with the bell lightly held. If it radiates, itdoes so minimally to the axilla. Mitral stenosis normally produces threedistinct abnormalities which can be heard: 1) A loud first sound 2) Anopening snap, and 3) A mid-diastolic rumble with a late diastolicaccentuation. A late diastolic murmur occurs in the latter half ofdiastole, synchronous with atrial contraction, and extends to the firstsound. Although occasionally occurring alone, it is usually a componentof the longer diastolic murmur of mitral stenosis or tricuspid stenosis.This murmur is low in frequency, and rumbling in quality. A continuousmurmur usually begins during systole and extends through the secondsound and throughout the diastolic period. It is usually produced as aresult of one of four mechanisms: 1) An abnormal communication betweenan artery and vein; 2) An abnormal communication between the aorta andthe right side of the heart or with the left atrium; 3) An abnormalincrease in flow, or constriction in an artery; and 4) Increased orturbulent blood flow through veins. Patent Ductus Arteriosus (PDA) isthe classical example of this murmur. This condition is usuallycorrected in childhood. It is heard best at base left, and is usuallyeasily audible with the bell or diaphragm. Another example of acontinuous murmur is the so-called venous hum, but in this instance onehears a constant roaring sound which changes little with the cardiaccycle. A late systolic murmur begins in the latter half of systole,peaks in the later third of systole, and extends to the second sound. Itis a modified regurgitant murmur with a backward flow through anincompetent valve, usually the mitral valve. It is commonly heard inmitral valve prolapse, and is usually high in frequency (blowing inquality), and heard best with a diaphragm at the apex. It may radiate tothe axilla or left sternal border. A pansystolic or holosystolic murmuris heard continuously throughout systole. It begins with the first heartsound, and ends with the second heart sound. It is commonly heard inmitral regurgitation, tricuspid regurgitation, and ventricular septaldefect. This type of murmur is caused by backward blood flow. Since thepressure remains higher throughout systole in the ejecting chamber thanin the receiving chamber, the murmur is continuous throughout systole.Diastolic murmurs are sustained noises that are audible between S2 andthe next S. Unlike systolic murmurs, diastolic murmurs should usually beconsidered pathological, and not normal. Typical abnormalities causingdiastolic murmurs are aortic regurgitation, pulmonic regurgitation,mitral stenosis, and tricuspid stenosis. The timing of diastolic murmursis the primary concern of this program. These murmurs can be early, mid,late and pan in nature. In a pericardial friction rub, there are threesounds, one systolic, and two diastolic. The systolic sound may occuranywhere in systole, and the two diastolic sounds occur at the times theventricles are stretched. This stretching occurs in early diastole, andat the end of diastole. The pericardial friction rub has a scratching,grating, or squeaking leathery quality. It tends to be high in frequencyand best heard with a diaphragm. A pericardial friction rub is a sign ofpericardial inflammation and may be heard in infective pericarditis, inmyocardial infarction, following cardiac surgery, trauma, and inautoimmune problems such as rheumatic fever.

In addition to heart sound analysis, the timing between the onset andoffset of particular features of the ECG (referred to as an interval)provides a measure of the state of the heart and can indicate thepresence of certain cardiological conditions.

In addition to providing beat-to-beat timing information for othersensors to use, the patterns of the constituent waveform featuresdetermined by the HMM or neural networks, among other classifiers, canbe used for detecting heart attacks or stroke attacks, among others. Forexample, the detection and classification of ventricular complexes fromthe ECG data is can be used for rhythm and various types of arrhythmiato be recognized. The system analyzes pattern recognition parameters forclassification of normal QRS complexes and premature ventricularcontractions (PVC). Exemplary parameters include the width of the QRScomplex, vectorcardiogram parameters, amplitudes of positive andnegative peaks, area of positive and negative waves, varioustime-interval durations, amplitude and angle of the QRS vector, amongothers. The EKG analyzer can analyze EKG/ECG patterns for Hypertrophy,Enlargement of the Heart, Atrial Enlargement, Ventricular Hypertrophy,Arrhythmias, Ectopic Supraventricular Arrhythmias, VentricularTachycardia (VT), Paroxysmal Supraventricular Tachycardia (PSVT),Conduction Blocks, AV Block, Bundle Branch Block, Hemiblocks,Bifascicular Block, Preexcitation Syndromes, Wolff-Parkinson-WhiteSyndrome, Lown-Ganong-Levine Syndrome, Myocardial Ischemia, Infarction,Non-Q Wave Myocardial Infarction, Angina, Electrolyte Disturbances,Heart Attack, Stroke Attack, Hypothermia, Pulmonary Disorder, CentralNervous System Disease, or Athlete's Heart, for example.

FIG. 4C shows an exemplary process to detect stroke attack. In thisembodiment, 3D accelerometer sensing is used. First, the process looksfor weakness (hemiparesis) in either the left half or the right half ofthe body, for example the left/right arms, legs, or face (3000). Next,the system analyzes walking pattern to see if the patient has a loss ofbalance or coordination (3002). The system then asks the user to movehands/feet in a predetermined pattern (3004) and reads accelerometeroutput in accordance with predetermined pattern movement (3006). Forexample, the system can ask the user to point his/her right or left handto the nose. The accelerometer outputs are tested to check if thecorrect hand did reach the nose. In another example, the user can beprompted to extend his or her hands on both side and wiggle the hands orto kick the legs. Again, the outputs of the accelerometers are used toconfirm that the user is able to follow direction. The accelerometeroutputs are provided to a pattern classifier, which can be an HMM, aneural network, a Bayesian network, fuzzy logic, or any suitableclassifiers (3008). The system also checks whether patient isexperiencing dizziness or sudden, severe headache with no known cause(3010). Next, the system displays a text image and asks the patient toread back the text image, one eye at a time (3012). Using a speechrecognizer module, the user speech is converted into text to compareagainst the text image. The speech recognizer also detects if the userexhibits signs of confusion, trouble speaking or understanding (3014).The system also asks the patient if they feel numbness in the body-arms,legs, face (3016). Next the system asks the patient to squeezegauge/force sensor to determine force applied during squeeze (3018). Ifany of the above tests indicate a possible stroke, the system displays awarning to the patient and also connects the patient to the appropriateemergency response authority, family member, or physician.

In one implementation, an HMM is used to track patient motor skills orpatient movement patterns. Human movement involves a periodic motion ofthe legs. Regular walking involves the coordination of motion at thehip, knee and ankle, which consist of complex joints. The musculargroups attached at various locations along the skeletal structure oftenhave multiple functions. The majority of energy expended during walkingis for vertical motion of the body. When a body is in contact with theground, the downward force due to gravity is reflected back to the bodyas a reaction to the force. When a person stands still, this groundreaction force is equal to the person's weight multiplied bygravitational acceleration. Forces can act in other directions. Forexample, when we walk, we also produce friction forces on the ground.When the foot hits the ground at a heel strike, the friction between theheel and the ground causes a friction force in the horizontal plane toact backwards against the foot. This force therefore causes a breakingaction on the body and slows it down. Not only do people accelerate andbrake while walking, they also climb and dive. Since reaction force ismass times acceleration, any such acceleration of the body will bereflected in a reaction when at least one foot is on the ground. Anupwards acceleration will be reflected in an increase in the verticalload recorded, while a downwards acceleration will be reduce theeffective body weight. Zigbee wireless sensors with tri-axialaccelerometers are mounted to the patient on different body locationsfor recording, for example the tree structure as shown in FIG. 4D. Asshown therein, sensors can be placed on the four branches of the linksconnect to the root node (torso) with the connected joint, left shoulder(LS), right shoulder (RS), left hip (LH), and right hip (RH).Furthermore, the left elbow (LE), right elbow (RE), left knee (LK), andright knee (RK) connect the upper and the lower extremities. Thewireless monitoring devices can also be placed on upper back body nearthe neck, mid back near the waist, and at the front of the right legnear the ankle, among others.

The sequence of human motions can be classified into several groups ofsimilar postures and represented by mathematical models calledmodel-states. A model-state contains the extracted features of bodysignatures and other associated characteristics of body signatures.Moreover, a posture graph is used to depict the inter-relationshipsamong all the model-states, defined as PG(ND,LK), where ND is a finiteset of nodes and LK is a set of directional connections between everytwo nodes. The directional connection links are called posture links.Each node represents one model-state, and each link indicates atransition between two model-states. In the posture graph, each node mayhave posture links pointing to itself or the other nodes.

In the pre-processing phase, the system obtains the human body profileand the body signatures to produce feature vectors. In the modelconstruction phase, the system generates a posture graph, examinefeatures from body signatures to construct the model parameters of HMM,and analyze human body contours to generate the model parameters ofASMs. In the motion analysis phase, the system uses features extractedfrom the body signature sequence and then applies the pre-trained HMM tofind the posture transition path, which can be used to recognize themotion type. Then, a motion characteristic curve generation procedurecomputes the motion parameters and produces the motion characteristiccurves. These motion parameters and curves are stored over time, and ifdifferences for the motion parameters and curves over time is detected,the system then runs the patient through additional tests to confirm astroke attack, and if a stroke attack is suspected, the system promptsthe user to seek medical attention immediately and preferably within the3 hour for receiving TPA.

FIG. 4E shows one exemplary process for determining weakness in the leftor right half of the body. The process compares historical left shoulder(LS) strength against current LS strength (3200). The process alsocompares historical right shoulder (RS) strength against current RSstrength (3202). The process can compare historical left hip (LH)strength against current LH strength (3204). The process can alsocompare historical right hip (RH) strength against current RH strength(3206). If the variance between historical and current strength exceedsthreshold, the process generates warnings (3208). Furthermore, similarcomparisons can be made for sensors attached to the left elbow (LE),right elbow (RE), left knee (LK), and right knee (RK) connect the upperand the lower extremities, among others.

The system can ask the patient to squeeze a strength gauge,piezoelectric sensor, or force sensor to determine force applied duringsqueeze. The user holds the sensor or otherwise engages the sensor. Theuser then applies and holds a force (e.g., compression, torque, etc.) tothe sensor, which starts a timer clock and triggers a sampling startindicator to notify the user to continue to apply (maximum) force to thesensor. Strength measurements are then sampled periodically during thesampling period until the expiration of time. From the sampled strengthdata, certain strength measurement values are selected, such as themaximum value, average value(s), or values obtained during the samplingperiod. The user can test both hands at the same time, or alternativelyhe may test one hand at a time. A similar approach is used to sense legstrength, except that the user is asked to pushed down on a scale todetermine the foot force generated by the user.

The system can detect hemiparesis, a very common symptom of stroke, bydetecting muscular weakness or partial paralysis to one side of thebody. Additionally, the accelerometers can detect ataxia, which is animpaired ability to perform smooth coordinated voluntary movements.Additionally, the system can detect aphasia, including receptive aphasiaand expressive aphasia. Aphasia is a cognitive disorder marked by animpaired ability to comprehend (receptive aphasia) or express(expressive aphasia) language. Exemplary embodiments are disclosed fordetecting receptive aphasia by displaying text or playing verbalinstructions to the user, followed by measuring the correctness and/ortime delay of the response from the user. Exemplary embodiments are alsodisclosed for detecting expressive aphasia by positing sound made by ananimal to the user, prompting the user to identify or name the animal,and measuring the correctness and/or time delay of the response from theuser. The system can also detect dysarthria, a disorder of speecharticulation (e.g., slurred speech), by prompting the user to say a wordor phrase that is recorded for subsequent comparison by voice patternrecognition or evaluation by medical personnel.

In the above manner, the system automatically reminds the user to gethelp if he feels a sudden numbness or weakness of the face, arm or leg,especially on one side of the body, sudden confusion, trouble speakingor understanding, sudden trouble seeing in one or both eyes, or suddentrouble walking, dizziness, loss of balance or coordination.

In one embodiment, the accelerometers distinguish between lying down andeach upright position of sitting and standing based on the continuousoutput of the 3D accelerometer. The system can detect (a) extended timein a single position; (b) extended time sitting in a slouching posture(kyphosis) as opposed to sitting in an erect posture (lordosis); and (c)repetitive stressful movements, such as may be found on somemanufacturing lines, while typing for an extended period of time withoutproper wrist support, or while working all day at a job lifting boxes,among others. In one alternative embodiment, angular position sensors,one on each side of the hip joint, can be used to distinguish lyingdown, sitting, and standing positions. In another embodiment, thepresent invention repeatedly records position and/or posture data overtime. In one embodiment, magnetometers can be attached to a thigh andthe torso to provide absolute rotational position about an axiscoincident with Earth's gravity vector (compass heading, or yaw). Inanother embodiment, the rotational position can be determined throughthe in-door positioning system as discussed above.

Depending on the severity of the stroke, patients can experience a lossof consciousness, cognitive deficits, speech dysfunction, limb weakness,hemiplegia, vertigo, diplopia, lower cranial nerve dysfunction, gazedeviation, ataxia, hemianopia, and aphasia, among others. Four classicsyndromes that are characteristically caused by lacunar-type stroke are:pure motor hemiparesis, pure sensory syndrome, ataxic hemiparesissyndrome, and clumsy-hand dysarthria syndrome. Patients with pure motorhemiparesis present with face, arm, and leg weakness. This conditionusually affects the extremities equally, but in some cases it affectsone extremity more than the other. The most common stroke location inaffected patients is the posterior limb of the internal capsule, whichcarries the descending corticospinal and corticobulbar fibers. Otherstroke locations include the pons, midbrain, and medulla. Pure sensorysyndrome is characterized by hemibody sensory symptoms that involve theface, arm, leg, and trunk. It is usually the result of an infarct in thethalamus. Ataxic hemiparesis syndrome features a combination ofcerebellar and motor symptoms on the same side of the body. The leg istypically more affected than the arm. This syndrome can occur as aresult of a stroke in the pons, the internal capsule, or the midbrain,or in the anterior cerebral artery distribution. Patients withclumsy-hand dysarthria syndrome experience unilateral hand weakness anddysarthria. The dysarthria is often severe, whereas the hand involvementis more subtle, and patients may describe their hand movements as“awkward.” This syndrome is usually caused by an infarct in the pons.

Different patterns of signs can provide clues as to both the locationand the mechanism of a particular stroke. The system can detect symptomssuggestive of a brainstem stroke include vertigo, diplopia, bilateralabnormalities, lower cranial nerve dysfunction, gaze deviation (towardthe side of weakness), and ataxia. Indications of higher corticaldysfunction—such as neglect, hemianopsia, aphasia, and gaze preference(opposite the side of weakness)—suggest hemispheric dysfunction withinvolvement of a superficial territory from an atherothrombotic orembolic occlusion of a mainstem vessel or peripheral branch.

The system can detect a pattern of motor weakness. Ischemia of thecortex supplied by the middle cerebral artery typically causes weaknessthat (1) is more prominent in the arm than in the leg and (2) involvesthe distal muscles more than the proximal muscles. Conversely,involvement of an area supplied by the superficial anterior cerebralartery results in weakness that (1) is more prominent in the leg thanthe arm and (2) involves proximal upper extremity (shoulder) musclesmore than distal upper extremity muscles. Flaccid paralysis of both thearm and leg (unilateral) suggests ischemia of the descending motortracts in the basal ganglia or brainstem. This is often caused by anocclusion of a penetrating artery as a result of small-vessel disease.Once the stroke is detected, intravenous (IV) tissue plasminogenactivator (t-PA) needs to be given within 3 hours of symptom onset. Anaccurate assessment of the timing of the stroke is also crucial. Thesystem keeps track of the timing off the onset of the stroke for thispurpose.

In yet a further embodiment for performing motor motion analysis, an HMMis used to determine the physical activities of a patient, to monitoroverall activity levels and assess compliance with a prescribed exerciseregimen and/or efficacy of a treatment program. The HMM may also measurethe quality of movement of the monitored activities. For example, thesystem may be calibrated or trained in the manner previously described,to recognize movements of a prescribed exercise program. Motor functioninformation associated with the recognized movements may be sent to theserver for subsequent review. A physician, clinician, or physicaltherapist with access to patient data may remotely monitor compliancewith the prescribed program or a standardized test on motor skill. Forexample, patients can take the Wolf Motor Function test and accelerationdata is captured on the following tasks:

placing the forearm on a table from the side

moving the forearm from the table to a box on the table from the side

extending the elbow to the side

extending the elbow to the side against a light weight

placing the hand on a table from the front

moving the hand from table to box

flexing the elbow to retrieve a light weight

lifting a can of water

lifting a pencil, lifting a paper clip

stacking checkers, flipping cards

turning a key in a lock

folding a towel

lifting a basket from the table to a shelf above the table.

The above system can be implemented using a person computer or can beimplemented on a game machine such as Nintendo's Wii. The Wii controllerhas the ability to sense its position in three-dimensional space inrelation to the television set. After placing sensors next to thetelevision, players will have the ability to hold the controller (orfreehand as many call it) with one hand in the same way they would atelevision remote. The three known input methods the freehand allowsare:

-   -   Standard button input, such as pressing the “A” button or D-pad        to execute a move.    -   Three dimensional input, which gives players the ability to move        or rotate the freehand controller in different directions on any        axis.    -   Pointer functionality, which turns the controller into a mouse        of sorts, with the ability to move a cursor around on screen.

The Wii controller—called the Wii Remote by Nintendo, dubbed the Wiimoteby fans—also connects to several peripherals at the end of thecontroller. A built-in speaker system can be placed in the middle of thecontroller, allowing players to hear sounds not only from thetelevisions but transferred directly to the controller.

“Computer readable media” can be any available media that can beaccessed by client/server devices. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store the desired information and which can beaccessed by client/server devices. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia.

All references including patent applications and publications citedherein are incorporated herein by reference in their entirety and forall purposes to the same extent as if each individual publication orpatent or patent application was specifically and individually indicatedto be incorporated by reference in its entirety for all purposes. Manymodifications and variations of this invention can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only. The above specification, examples anddata provide a complete description of the manufacture and use of thecomposition of the invention. Since many embodiments of the inventioncan be made without departing from the spirit and scope of theinvention, the invention resides in the claims hereinafter appended.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the spirit and scope of theinvention as defined by the appended claims.

1. A heart monitoring system for a patient, comprising: an 802 protocoltransmitter; an 802 protocol receiver adapted to communicate with the802 transmitter, the 802 protocol transmitter and receiver forming aDoppler radar to detect heartbeat motion on a chest; and an analyzercoupled to one of the transmitter and receiver to determine heart attackor stroke attack.
 2. The system of claim 1, wherein the analyzeridentifies one patient from another based on heart rate signaturecharacteristics.
 3. The system of claim 1, comprising a sound transducercoupled to the transmitter to communicate audio over a POTS network or aPSTN network.
 4. The system of claim 1, comprising one of: an EMGdetector, EEG detector, an EKG detector, an ECG detector, anelectromagnetic detector, an ultrasonic detector, an optical detector, aHidden Markov Model (HMM) recognizer, a dynamic time warp (DTW)recognizer, a neural network, a fuzzy logic engine, a Bayesian network.5. The system of claim 1, wherein the appliance monitors patientmovement.
 6. The system of claim 1, comprising an in-door positioningsystem coupled to one or more mesh network appliances to providelocation information.
 7. The system of claim 1, comprising a call centercoupled to the analyzer to provide a human response.
 8. The system ofclaim 1, wherein the 802.11 protocol comprises one of: 802.11 protocol,802.15 protocol, 802.16 protocol, WiFi protocol, WiMAX protocol.
 9. Thesystem of claim 1, comprising a wireless router coupled to the meshnetwork and wherein the wireless router comprises one of: 802.11 router,802.16 router, WiFi router, WiMAX router, Bluetooth router, X10 router.10. The system of claim 1, comprising a mesh network appliance coupledto a power line to communicate X10 data to and from the mesh network.11. The system of claim 1, wherein the transmitter transmits voice. 12.The system of claim 1, wherein the analyzer determines one of: totalbody water, compartmentalization of body fluids, cardiac monitoring,blood flow, skinfold thickness, dehydration, blood loss, woundmonitoring, ulcer detection, deep vein thrombosis, hypovolemia,hemorrhage, blood loss, heart attack, stroke attack.
 13. The system ofclaim 1, comprising code to store and analyze patient informationincluding vital sign rate of change, medicine taking habits, eating anddrinking habits, sleeping habits, or exercise habits.
 14. The system ofclaim 1, comprising one or more bioelectric contacts coupleable to thepatient from one of: a patch, a wristwatch, a band, a wristband, a chestband, a leg band, a sock, a glove, a foot pad, a head-band, an ear-clip,an ear phone, a shower-cap, an armband, an ear-ring, eye-glasses,sun-glasses, a belt, a sock, a shirt, a garment, a jewelry, a bedspread, a pillow cover, a pillow, a mattress, a blanket, each having oneor more sensors in communication with the wireless mesh network.
 15. Thesystem of claim 1, wherein the analyzer detects weakness in left halfand right half of patient body; detects walking pattern for loss ofbalance or coordination; asks user to move hands/feet in a predeterminedpattern; checking whether the person experienced dizziness or headache;displays a text image and ask the person to read back the text image oneeye at a time; using a speech recognizer to detect confusion, troublespeaking or understanding; and querying the person for body numbness.16. The system of claim 16, comprising calibrating Doppler radar signalwith an actual blood pressure during a training phase to develop a modeland using the model with Doppler radar signal during an operationalphase to estimate continuous blood pressure.
 17. A monitoring system fora person, comprising: wireless local area network (WLAN) transceiversoperating as a Doppler radar to wirelessly detect the person's heartparameter; and a processor coupled to the WLAN transceivers to determinea stroke attack.
 18. The system of claim 17, wherein the processordetects weakness in left half and right half of patient body; detectswalking pattern for loss of balance or coordination; asks user to movehands/feet in a predetermined pattern; checking whether the personexperienced dizziness or headache; displays a text image and ask theperson to read back the text image one eye at a time; using a speechrecognizer to detect confusion, trouble speaking or understanding; andquerying the person for body numbness.
 19. The system of claim 17,wherein the processor determines one person from another using eachperson's heartbeat signature.
 20. The system of claim 17, wherein theprocessor analyzes patient information including one of: vital sign rateof change, medicine taking habits, eating and drinking habits, sleepinghabits, exercise habits.
 21. The system of claim 17, wherein theprocessor detects weakness in left half and right half of patient body;detects walking pattern for loss of balance or coordination; asks userto move hands/feet in a predetermined pattern; checking whether theperson experienced dizziness or headache; displays a text image and askthe person to read back the text image one eye at a time; using a speechrecognizer to detect confusion, trouble speaking or understanding; andquerying the person for body numbness.
 22. The system of claim 17,comprising calibrating Doppler radar signal with an actual bloodpressure during a training phase to develop a model and using the modelwith Doppler radar signal during an operational phase to estimatecontinuous blood pressure.